{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Предсказание стоимости автотранспорта с помощью методов анализа данных\n",
    "\n",
    "Транспортная отрасль входит в пятерку отраслей, в которых методы больше всего используют искусственный интеллект и анализ данных. Современные автомобили имеют множество интеллектуальных систем, которые помогают водителям как непосредственно во время вождения, так и в задачах использования встроенного мультимедиа. Однако алгоритмы анализа данных можно применять и в других аспектах, связанных с автомобилями, например, для предсказания стоимости автомобиля, которая будет основана на множестве характеристик транспортного средства.\n",
    "\n",
    "На стоимость подержанного автомобиля влияет множество факторов, среди которых выделяют несколько основных:\n",
    "\n",
    "- Марка автомобиля\n",
    "- Тип трансмиссии\n",
    "- Привод автомобиля\n",
    "- Год выпуска\n",
    "- Кузов автомобиля\n",
    "- Состояние салона\n",
    "- Техническое состояние (включает в себя много отдельных характеристик)\n",
    "- Пробег\n",
    "- Дополнительные технологии\n",
    "\n",
    "Сумма всех факторов создают большую сложность для простого пользователя авто при продаже, ведь учитывая весь спектр особенностей своего транспортного средства необходимо назначить правильную стоимость. Поэтому в этой работе проводится определение класса автомобиля (бюджетный, средний, дорогой) с помощью методов машинного обучения).\n",
    "\n",
    "Для проведения данного исследования был взят датасет \"Used Cars Dataset (CarDekho)\". Ссылка на датасет: https://www.kaggle.com/datasets/sukritchatterjee/used-cars-dataset-cardekho"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "5428fd8cb0db4f8c"
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Этап №1. Получение и предобработка данных\n",
    "\n",
    "Для начала необходимо подключить все необходимые библиотеки для работы. \n",
    "\n",
    "1. Pandas - используется для работы с данными в более удобном формате - DataFrame и все его методы \n",
    "2. NumPy - для числовых операций с данными\n",
    "3. matplotlib - для графического анализа выборки и построения графиков с результатами\n",
    "4. time - для сравнения времени, затраченного на работу моделей\n",
    "5. sklearn - для использования самих моделей и необходимых преобразований данных \n",
    "6. seaborn - для построения матриц ошибок"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "6cd1e0b6f88e5df6"
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "# data working\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# analysis\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "import seaborn as sns\n",
    "\n",
    "# prepare data\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# models\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:20.111705300Z",
     "start_time": "2023-12-22T18:07:19.085581600Z"
    }
   },
   "id": "dee754b04c669e3d"
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "outputs": [
    {
     "data": {
      "text/plain": "(37814, 140)"
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"archive/cars_details_merges.csv\", low_memory=False)\n",
    "df.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:14:23.940630800Z",
     "start_time": "2023-12-22T18:14:20.755768200Z"
    }
   },
   "id": "45bd74eee94fc34f"
  },
  {
   "cell_type": "markdown",
   "source": [
    "С помощью функции read_csv мы получили все данные из датасета и сохранили их в переменную df. Наш датасет имеет 140 столбцов и 37814 записей. Однако для работы не нужны все 140 столбцов, поэтому нужно отобрать только необходимые из них. А также используем вспомогательный датасет для того, чтобы получить стоимости автомобилей."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "b1bf42acdabe2170"
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "outputs": [
    {
     "data": {
      "text/plain": "   myear         bt      tt   ft        km     oem  Displacement  Gear Box  \\\n0   2016  Hatchback  Manual  CNG    69,162  Maruti         998.0   5 Speed   \n1   2015  Hatchback  Manual  CNG    45,864  Maruti         998.0  5 Speed    \n2   2015      Sedan  Manual  CNG    81,506   Honda        1198.0   5 Speed   \n3   2013  Hatchback  Manual  CNG  1,15,893  Maruti         998.0   5 Speed   \n4   2022        MUV  Manual  CNG    18,900  Maruti        1462.0   5-Speed   \n\n  Drive Type  Seating Capacity   utype  No of Cylinder  Values per Cylinder  \\\n0        FWD               5.0  Dealer             3.0                  4.0   \n1        FWD               5.0  Dealer             3.0                  4.0   \n2        FWD               5.0  Dealer             4.0                  4.0   \n3        FWD               5.0  Dealer             3.0                  4.0   \n4        2WD               7.0  Dealer             4.0                  4.0   \n\n  Turbo Charger   Color  listed_price  \n0            No  Silver      370000.0  \n1            No    Grey      365000.0  \n2            No  Silver      421000.0  \n3            No  Silver      240000.0  \n4           NaN   White     1175000.0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>myear</th>\n      <th>bt</th>\n      <th>tt</th>\n      <th>ft</th>\n      <th>km</th>\n      <th>oem</th>\n      <th>Displacement</th>\n      <th>Gear Box</th>\n      <th>Drive Type</th>\n      <th>Seating Capacity</th>\n      <th>utype</th>\n      <th>No of Cylinder</th>\n      <th>Values per Cylinder</th>\n      <th>Turbo Charger</th>\n      <th>Color</th>\n      <th>listed_price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2016</td>\n      <td>Hatchback</td>\n      <td>Manual</td>\n      <td>CNG</td>\n      <td>69,162</td>\n      <td>Maruti</td>\n      <td>998.0</td>\n      <td>5 Speed</td>\n      <td>FWD</td>\n      <td>5.0</td>\n      <td>Dealer</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>No</td>\n      <td>Silver</td>\n      <td>370000.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2015</td>\n      <td>Hatchback</td>\n      <td>Manual</td>\n      <td>CNG</td>\n      <td>45,864</td>\n      <td>Maruti</td>\n      <td>998.0</td>\n      <td>5 Speed</td>\n      <td>FWD</td>\n      <td>5.0</td>\n      <td>Dealer</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>No</td>\n      <td>Grey</td>\n      <td>365000.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2015</td>\n      <td>Sedan</td>\n      <td>Manual</td>\n      <td>CNG</td>\n      <td>81,506</td>\n      <td>Honda</td>\n      <td>1198.0</td>\n      <td>5 Speed</td>\n      <td>FWD</td>\n      <td>5.0</td>\n      <td>Dealer</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>No</td>\n      <td>Silver</td>\n      <td>421000.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2013</td>\n      <td>Hatchback</td>\n      <td>Manual</td>\n      <td>CNG</td>\n      <td>1,15,893</td>\n      <td>Maruti</td>\n      <td>998.0</td>\n      <td>5 Speed</td>\n      <td>FWD</td>\n      <td>5.0</td>\n      <td>Dealer</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>No</td>\n      <td>Silver</td>\n      <td>240000.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2022</td>\n      <td>MUV</td>\n      <td>Manual</td>\n      <td>CNG</td>\n      <td>18,900</td>\n      <td>Maruti</td>\n      <td>1462.0</td>\n      <td>5-Speed</td>\n      <td>2WD</td>\n      <td>7.0</td>\n      <td>Dealer</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>White</td>\n      <td>1175000.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['myear', \n",
    "         'bt', \n",
    "         'tt', \n",
    "         'ft',\n",
    "         'km',\n",
    "         'oem',\n",
    "         'Displacement',\n",
    "         'Gear Box',\n",
    "         'Drive Type',\n",
    "         'Seating Capacity',\n",
    "         'utype',\n",
    "         'No of Cylinder',\n",
    "         'Values per Cylinder',\n",
    "         'Turbo Charger',\n",
    "         'Color'\n",
    "         ]]\n",
    "\n",
    "supp_df = pd.read_csv(\"archive/cars_data_clean.csv\")\n",
    "df['listed_price'] = supp_df['listed_price']\n",
    "\n",
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:14:31.324602Z",
     "start_time": "2023-12-22T18:14:30.208363600Z"
    }
   },
   "id": "88460ccaa9496e3a"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Теперь датафрейм имеет 15 колонок, однако данные необходимо представить в том виде, в котором с ними можно будет работать. В первую очередь необходимо переименовать колонки."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "7f07922b67bfbab1"
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [
    {
     "data": {
      "text/plain": "799"
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.Color.unique())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:14:44.587737900Z",
     "start_time": "2023-12-22T18:14:44.505019900Z"
    }
   },
   "id": "43cc229820f04817"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "   ModelYear   BodyType TransmissionType FuelType KmMileage   Brand  \\\n0       2016  Hatchback           Manual      CNG    69,162  Maruti   \n1       2015  Hatchback           Manual      CNG    45,864  Maruti   \n2       2015      Sedan           Manual      CNG    81,506   Honda   \n3       2013  Hatchback           Manual      CNG  1,15,893  Maruti   \n4       2022        MUV           Manual      CNG    18,900  Maruti   \n\n   Clearance GearNumber DriveType  SeatsNumber SellerType  CylinderNumber  \\\n0      998.0    5 Speed       FWD          5.0     Dealer             3.0   \n1      998.0   5 Speed        FWD          5.0     Dealer             3.0   \n2     1198.0    5 Speed       FWD          5.0     Dealer             4.0   \n3      998.0    5 Speed       FWD          5.0     Dealer             3.0   \n4     1462.0    5-Speed       2WD          7.0     Dealer             4.0   \n\n   ValvesNumber TurboCharger  ListedPrice  \n0           4.0           No     370000.0  \n1           4.0           No     365000.0  \n2           4.0           No     421000.0  \n3           4.0           No     240000.0  \n4           4.0          NaN    1175000.0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ModelYear</th>\n      <th>BodyType</th>\n      <th>TransmissionType</th>\n      <th>FuelType</th>\n      <th>KmMileage</th>\n      <th>Brand</th>\n      <th>Clearance</th>\n      <th>GearNumber</th>\n      <th>DriveType</th>\n      <th>SeatsNumber</th>\n      <th>SellerType</th>\n      <th>CylinderNumber</th>\n      <th>ValvesNumber</th>\n      <th>TurboCharger</th>\n      <th>ListedPrice</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2016</td>\n      <td>Hatchback</td>\n      <td>Manual</td>\n      <td>CNG</td>\n      <td>69,162</td>\n      <td>Maruti</td>\n      <td>998.0</td>\n      <td>5 Speed</td>\n      <td>FWD</td>\n      <td>5.0</td>\n      <td>Dealer</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>No</td>\n      <td>370000.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2015</td>\n      <td>Hatchback</td>\n      <td>Manual</td>\n      <td>CNG</td>\n      <td>45,864</td>\n      <td>Maruti</td>\n      <td>998.0</td>\n      <td>5 Speed</td>\n      <td>FWD</td>\n      <td>5.0</td>\n      <td>Dealer</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>No</td>\n      <td>365000.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2015</td>\n      <td>Sedan</td>\n      <td>Manual</td>\n      <td>CNG</td>\n      <td>81,506</td>\n      <td>Honda</td>\n      <td>1198.0</td>\n      <td>5 Speed</td>\n      <td>FWD</td>\n      <td>5.0</td>\n      <td>Dealer</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>No</td>\n      <td>421000.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2013</td>\n      <td>Hatchback</td>\n      <td>Manual</td>\n      <td>CNG</td>\n      <td>1,15,893</td>\n      <td>Maruti</td>\n      <td>998.0</td>\n      <td>5 Speed</td>\n      <td>FWD</td>\n      <td>5.0</td>\n      <td>Dealer</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>No</td>\n      <td>240000.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2022</td>\n      <td>MUV</td>\n      <td>Manual</td>\n      <td>CNG</td>\n      <td>18,900</td>\n      <td>Maruti</td>\n      <td>1462.0</td>\n      <td>5-Speed</td>\n      <td>2WD</td>\n      <td>7.0</td>\n      <td>Dealer</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>1175000.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.rename(columns={'myear': \"ModelYear\",\n",
    "            'bt': 'BodyType',\n",
    "            'tt': \"TransmissionType\",\n",
    "            'ft': 'FuelType',\n",
    "            'km': 'KmMileage',\n",
    "            'oem': 'Brand',\n",
    "            'Displacement': 'Clearance',\n",
    "            'Gear Box': 'GearNumber',\n",
    "            'Drive Type': 'DriveType',\n",
    "            'Seating Capacity': 'SeatsNumber',\n",
    "            'listed_price': 'ListedPrice',\n",
    "            'utype': \"SellerType\",\n",
    "            'No of Cylinder': \"CylinderNumber\",\n",
    "            'Values per Cylinder': 'ValvesNumber',\n",
    "            'Turbo Charger': 'TurboCharger'\n",
    "           })\n",
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:23.683976200Z",
     "start_time": "2023-12-22T18:07:23.651908700Z"
    }
   },
   "id": "8afc16df83cb71f0"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Теперь проверим записи на наличие нулевых значений."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "12f7cd30c2ade833"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "ModelYear              0\nBodyType              19\nTransmissionType       0\nFuelType               0\nKmMileage              0\nBrand                  0\nClearance             53\nGearNumber           407\nDriveType           4496\nSeatsNumber           18\nSellerType             0\nCylinderNumber       143\nValvesNumber         228\nTurboCharger        2176\nListedPrice            1\ndtype: int64"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:23.969738500Z",
     "start_time": "2023-12-22T18:07:23.683976200Z"
    }
   },
   "id": "11481fed8130b2bc"
  },
  {
   "cell_type": "markdown",
   "source": [
    "В нескольких колонках встречаются нулевые значения, однако, сейчас мы не можем их убрать, так как при удалении записей потеряется достаточно большое количество данных. Поэтому в первую очередь качественные характеристики необходимо перевести в числовой вид.\n",
    "\n",
    "Переведем в числовой вид кузов автомобиля."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "34c885920a7809d4"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "label_encode = {\"BodyType\":\n",
    "                    {\n",
    "                        \"Hatchback\": 0,\n",
    "                        \"Sedan\": 1,\n",
    "                        \"MUV\": 2,\n",
    "                        \"Minivans\": 3,\n",
    "                        \"Pickup Trucks\": 4,\n",
    "                        \"SUV\": 5, \n",
    "                        \"Luxury Vehicles\": 6,\n",
    "                        \"Convertibles\": 7,\n",
    "                        \"Coupe\": 8,\n",
    "                        \"Wagon\": 9,\n",
    "                        \"Hybrids\": 10\n",
    "                     }}\n",
    "df.replace(label_encode,inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:23.999580700Z",
     "start_time": "2023-12-22T18:07:23.744273100Z"
    }
   },
   "id": "d30b8f839dcd39c5"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Переведем в числовой вид тип трансмиссии."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c36300b7fd27385f"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "label_encode = {\n",
    "                \"TransmissionType\":\n",
    "                    {\n",
    "                        \"Manual\": 0,\n",
    "                        \"Automatic\": 1\n",
    "                    }}\n",
    "df.replace(label_encode,inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:24.020490200Z",
     "start_time": "2023-12-22T18:07:23.786535100Z"
    }
   },
   "id": "fb1e111d5204d479"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Переведем в числовой вид тип используемого топлива."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "94a687d6f9b77cb9"
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "label_encode = {\n",
    "                \"FuelType\":\n",
    "                    {\n",
    "                        \"CNG\": 0,\n",
    "                        \"LPG\": 1,\n",
    "                        \"Electric\": 2,\n",
    "                        \"Diesel\": 3,\n",
    "                        \"Petrol\": 4\n",
    "                    }}\n",
    "df.replace(label_encode,inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:24.085777900Z",
     "start_time": "2023-12-22T18:07:23.810947700Z"
    }
   },
   "id": "8a06652568e9be4e"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Переведем в числовой вид привод автомобиля."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "47fe54fce0920532"
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "label_encode = {\n",
    "                \"DriveType\":\n",
    "                    {\n",
    "                        \"FWD\": 0,\n",
    "                        \"RWD\": 1,\n",
    "                        \"AWD\": 2,\n",
    "                        \"2WD\": 3,\n",
    "                        \"Two Wheel Drive\": 3, \n",
    "                        \"FWD \": 0,\n",
    "                        \"2 WD\": 3,\n",
    "                        \"4WD\": 2,\n",
    "                        \"4 WD\": 2,\n",
    "                        \"Front Wheel Drive\": 0,\n",
    "                        \"All Wheel Drive\": 2,\n",
    "                        \"4X2\": 3,\n",
    "                        \"2wd\": 3, \n",
    "                        \"Four Whell Drive\": 2,\n",
    "                        \"Rear Wheel Drive with ESP\": 1,\n",
    "                        \"All-wheel drive with Electronic Traction\": 3,\n",
    "                        \"Rear-wheel drive with ESP\": 1,\n",
    "                        \"Permanent all-wheel drive quattro\": 2,\n",
    "                        \"Two Whhel Drive\": 3,\n",
    "                        \"4x4\": 2,\n",
    "                        \"4X4\": 2,\n",
    "                        \"4x2\": 3,\n",
    "                        \"RWD(with MTT)\": 1,\n",
    "                        \"3\": 3\n",
    "                    }}\n",
    "df.replace(label_encode,inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:24.209377300Z",
     "start_time": "2023-12-22T18:07:23.850629100Z"
    }
   },
   "id": "69e7a3ca7c3752e2"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Переведем в числовой вид марку автомобиля."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c555f8ebe41a6896"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "label_encode = {\n",
    "                \"Brand\":\n",
    "                    {\n",
    "                        'Maruti': 0,\n",
    "                        'Honda': 1,\n",
    "                        'Hyundai': 2,\n",
    "                        'Tata': 3,\n",
    "                        'Toyota': 4,\n",
    "                        'Bajaj': 5,\n",
    "                        'Mahindra': 6,\n",
    "                        'Chevrolet': 7,\n",
    "                        'MG': 8,\n",
    "                        'Mercedes-Benz': 9,\n",
    "                        'Mini': 10,\n",
    "                        'Renault': 11,\n",
    "                        'Nissan': 12,\n",
    "                        'Datsun': 13,\n",
    "                        'Kia': 14, \n",
    "                        'Force': 15,\n",
    "                        'ICML': 16, \n",
    "                        'Ashok Leyland': 17,\n",
    "                        'BMW': 18, \n",
    "                        'Volvo': 19,\n",
    "                        'Audi': 20, \n",
    "                        'Isuzu': 21,\n",
    "                        'Porsche': 22, \n",
    "                        'Jaguar': 23, \n",
    "                        'Lamborghini': 24,\n",
    "                        'Ford': 25, \n",
    "                        'Ferrari': 26,\n",
    "                        'DC': 27, \n",
    "                        'Bentley': 28,\n",
    "                        'Aston Martin': 29,\n",
    "                        'Volkswagen': 30, \n",
    "                        'Skoda': 31,\n",
    "                        'Jeep': 32, \n",
    "                        'Fiat': 33, \n",
    "                        'Land Rover': 34,\n",
    "                        'Mitsubishi': 35,\n",
    "                        'Mahindra Ssangyong': 36,\n",
    "                        'Mahindra Renault': 37, \n",
    "                        'Citroen': 38, \n",
    "                        'Hindustan Motors': 39, \n",
    "                        'Premier': 40,\n",
    "                        'Rolls-Royce': 41, \n",
    "                        'Opel': 42,\n",
    "                        'Hummer': 43,\n",
    "                        'Maserati': 44,\n",
    "                        'Lexus': 45\n",
    "                    }}\n",
    "df.replace(label_encode,inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:24.251138700Z",
     "start_time": "2023-12-22T18:07:23.958572700Z"
    }
   },
   "id": "4a6d9484fc5a6248"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Переведем в числовой вид ступенчатость коробки передач."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "df6d072531cde97f"
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "label_encode = {\n",
    "                \"GearNumber\":\n",
    "                    {\n",
    "                        '5 Speed': 5,\n",
    "                        '5 Speed ': 5,\n",
    "                        '5-Speed': 5,\n",
    "                        'Five Speed Manual Transmission': 5,\n",
    "                        '4 Speed': 4,\n",
    "                        '6 Speed': 6,\n",
    "                        '5 Speed+1(R)': 5,\n",
    "                        'Fully Automatic': 1,\n",
    "                        'Single Speed Automatic': 1,\n",
    "                        'Single speed reduction gear': 1,\n",
    "                        'Direct Drive': 1,\n",
    "                        'Single-speed transmission': 1,\n",
    "                        'Single Speed': 1,\n",
    "                        '7 Speed 9G-Tronic automatic': 7,\n",
    "                        '6-Speed': 6,\n",
    "                        '8-Speed': 8,\n",
    "                        '4-Speed': 4,\n",
    "                        '6-Speed iMT': 6,\n",
    "                        '7-Speed DCT': 7,\n",
    "                        '8 Speed': 8,\n",
    "                        '8 Speed Sport': 8,\n",
    "                        'Six Speed  Gearbox': 6,\n",
    "                        '5 Speed Forward, 1 Reverse': 5,\n",
    "                        '5 Speed,5 Forward, 1 Reverse': 5,\n",
    "                        '7 Speed': 7,\n",
    "                        '9 Speed': 9,\n",
    "                        'AMG Speedshift 9G TCT Automatic': 9,\n",
    "                        '6 Speed Automatic': 6,\n",
    "                        '8-speed': 8,\n",
    "                        '9G-TRONIC': 9,\n",
    "                        '8-speed tiptronic': 8,\n",
    "                        'E-CVT': 1,\n",
    "                        '6 Speed with Sequential Shift': 6,\n",
    "                        'CVT': 1,\n",
    "                        '5-Speed ': 5,\n",
    "                        '6 Speed ': 6,\n",
    "                        '4 Speed ': 4,\n",
    "                        '5 Speed Manual': 5,\n",
    "                        '7 Speed CVT': 7,\n",
    "                        '6 Speed iMT': 6,\n",
    "                        '5 speed': 5,\n",
    "                        '6 Speed MT': 6,\n",
    "                        '7-Speed': 7,\n",
    "                        '10 Speed': 10,\n",
    "                        '10 speed': 10,\n",
    "                        '5 Gears ': 5,\n",
    "                        '7 Speed DSG': 7,\n",
    "                        '7-Speed DSG': 7,\n",
    "                        '5 Speed Manual Transmission': 5,\n",
    "                        'Six Speed Manual Transmission': 6,\n",
    "                        'Six Speed Automatic Transmission': 6,\n",
    "                        '6-speed': 6,\n",
    "                        '5-speed': 5,\n",
    "                        '7-speed': 7,\n",
    "                        '7-speed DSG': 7,\n",
    "                        'Six Speed Manual': 6,\n",
    "                        '7 Speed 7G-DCT': 7,\n",
    "                        '7G DCT 7-Speed Dual Clutch Transmission ': 7,\n",
    "                        '9 speed Tronic': 9,\n",
    "                        '8-Speed DCT': 8,\n",
    "                        '7G-DCT': 7,\n",
    "                        '9G TRONIC': 9,\n",
    "                        '9G-TRONIC automatic': 9,\n",
    "                        '9-speed': 9,\n",
    "                        'AMG SPEEDSHIFT DCT 8G': 8,\n",
    "                        '7G-TRONIC Automatic Transmission': 7,\n",
    "                        'AMG 7-SPEED DCT': 7,\n",
    "                        '8-Speed Steptronic': 8,\n",
    "                        '8-Speed ': 8,\n",
    "                        '7-Speed Steptronic': 7,\n",
    "                        '8 Speed ': 8,\n",
    "                        'Automatic Transmission': 1,\n",
    "                        '8-Speed Steptronic Sport Automatic Transmission': 8,\n",
    "                        '8-Speed Automatic Transmission': 8,\n",
    "                        'IVT': 1,\n",
    "                        '7 Speed DCT': 7,\n",
    "                        '6-Speed IVT': 6,\n",
    "                        '6-Speed AT': 6,\n",
    "                        '7-Speed S-Tronic ': 7,\n",
    "                        '8 Speed Tiptronic': 8,\n",
    "                        '7-Speed S-Tronic': 7,\n",
    "                        '7-Speed S tronic': 7,\n",
    "                        '7-speed Stronic': 7,\n",
    "                        '7 Speed S tronic': 7,\n",
    "                        '7 Speed ': 7,\n",
    "                        '7 Speed S Tronic': 7,\n",
    "                        '8 Speed Multitronic': 8,\n",
    "                        '6-speed DCT': 6,\n",
    "                        '8 Speed CVT': 8,\n",
    "                        '8 speed': 8,\n",
    "                        '6-speed CVT': 6,\n",
    "                        '6-Speed DCT': 6,\n",
    "                        '9-Speed': 9,\n",
    "                        '9 -speed': 9,\n",
    "                        'Five Speed Manual Transmission Gearbox': 5,\n",
    "                        'Five Speed': 5,\n",
    "                        '5 speed manual': 5,\n",
    "                        'Five Speed Manual': 5,\n",
    "                        '8': 8,\n",
    "                        '6-Speed Automatic': 6,\n",
    "                        '9-speed automatic': 9,\n",
    "                        'Six Speed Automatic Gearbox': 6,\n",
    "                        '6 Speed Geartronic': 6,\n",
    "                        'Six Speed Geartronic, Six Speed Automati': 6,\n",
    "                        '8Speed': 8,\n",
    "                        '8-speed automatic': 8,\n",
    "                        '6 speed automatic': 6,\n",
    "                        'Six Speed Manual with Paddle Shifter': 6,\n",
    "                        '6': 6,\n",
    "                        '5': 5,\n",
    "                        '6 Speed AT': 6,\n",
    "                        '8 Speed Tip Tronic S': 8,\n",
    "                        '7-speed PDK': 7,\n",
    "                        '7 Speed dual clutch transmission': 7,\n",
    "                        '5 Speed CVT': 5,\n",
    "                        '6-Speed`': 6,\n",
    "                        '7-speed DCT': 7,\n",
    "                        'AGS': 1,\n",
    "                        '5 Speed AT+ Paddle Shifters': 5,\n",
    "                        'iMT': 1,\n",
    "                        '6-speed AutoSHIFT': 6,\n",
    "                        '6-Speed IMT': 6,\n",
    "                        '5-Speed`': 5,\n",
    "                        '5 Manual': 5,\n",
    "                        '6 speed ': 6,\n",
    "                        '6 Speed IMT': 6,\n",
    "                        '6 Speed IVT': 6,\n",
    "                        '6-speed IVT': 6,\n",
    "                        '5 - Speed': 5,\n",
    "                        '7 speed': 7,\n",
    "                        'Mercedes Benz 7 Speed Automatic': 7,\n",
    "                        'eCVT': 1\n",
    "                    }}\n",
    "df.replace(label_encode,inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:24.553754300Z",
     "start_time": "2023-12-22T18:07:24.097848400Z"
    }
   },
   "id": "309259e01d87f18f"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Переведём в числовой вид тип продавца"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "6bc77a51ee0deba7"
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "label_encode = {\n",
    "    \"SellerType\":\n",
    "        {\n",
    "            \"Dealer\": 0,\n",
    "            \"Individual\": 1\n",
    "        }}\n",
    "df.replace(label_encode,inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:24.578187100Z",
     "start_time": "2023-12-22T18:07:24.553754300Z"
    }
   },
   "id": "1c65275e065dac16"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Переведём в числовой вид наличие турбонаддува"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a2ff42dce1348d04"
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "label_encode = {\n",
    "    \"TurboCharger\":\n",
    "        {\n",
    "            \"No\": 0,\n",
    "            \"Yes\": 1,\n",
    "            'Twin': 1,\n",
    "            'no': 0,\n",
    "            'yes': 1,\n",
    "            'twin': 1,\n",
    "            'Turbo': 1,\n",
    "            'YES': 1,\n",
    "            'NO': 0\n",
    "        }}\n",
    "df.replace(label_encode,inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:24.644782900Z",
     "start_time": "2023-12-22T18:07:24.578187100Z"
    }
   },
   "id": "705ea737c643cd6d"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Переведем в числовой вид пробег автомобиля."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "dfc557a4aa0dcb04"
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "KmMileage = pd.Series([int(m.replace(\",\", \"\")) for m in list(df.KmMileage) if isinstance(m, str)])\n",
    "df.KmMileage = KmMileage"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:24.675744400Z",
     "start_time": "2023-12-22T18:07:24.635945900Z"
    }
   },
   "id": "3ebeeb9fa4b79a85"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Переведем в рубли стоимость автомобиля и выведем результат."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "2c8de782030aa788"
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "   ModelYear  BodyType  TransmissionType  FuelType  KmMileage  Brand  \\\n0       2016       0.0                 0         0      69162      0   \n1       2015       0.0                 0         0      45864      0   \n2       2015       1.0                 0         0      81506      1   \n3       2013       0.0                 0         0     115893      0   \n4       2022       2.0                 0         0      18900      0   \n\n   Clearance  GearNumber  DriveType  SeatsNumber  SellerType  CylinderNumber  \\\n0      998.0         5.0        0.0          5.0           0             3.0   \n1      998.0         5.0        0.0          5.0           0             3.0   \n2     1198.0         5.0        0.0          5.0           0             4.0   \n3      998.0         5.0        0.0          5.0           0             3.0   \n4     1462.0         5.0        3.0          7.0           0             4.0   \n\n   ValvesNumber  TurboCharger  ListedPrice  \n0           4.0           0.0     399600.0  \n1           4.0           0.0     394200.0  \n2           4.0           0.0     454680.0  \n3           4.0           0.0     259200.0  \n4           4.0           NaN    1269000.0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ModelYear</th>\n      <th>BodyType</th>\n      <th>TransmissionType</th>\n      <th>FuelType</th>\n      <th>KmMileage</th>\n      <th>Brand</th>\n      <th>Clearance</th>\n      <th>GearNumber</th>\n      <th>DriveType</th>\n      <th>SeatsNumber</th>\n      <th>SellerType</th>\n      <th>CylinderNumber</th>\n      <th>ValvesNumber</th>\n      <th>TurboCharger</th>\n      <th>ListedPrice</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2016</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>69162</td>\n      <td>0</td>\n      <td>998.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>399600.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2015</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>45864</td>\n      <td>0</td>\n      <td>998.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>394200.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2015</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>81506</td>\n      <td>1</td>\n      <td>1198.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>454680.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2013</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>115893</td>\n      <td>0</td>\n      <td>998.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>259200.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2022</td>\n      <td>2.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>18900</td>\n      <td>0</td>\n      <td>1462.0</td>\n      <td>5.0</td>\n      <td>3.0</td>\n      <td>7.0</td>\n      <td>0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>1269000.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ListedPrice = df.ListedPrice * 1.08\n",
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:24.731018700Z",
     "start_time": "2023-12-22T18:07:24.678485Z"
    }
   },
   "id": "5b0cbe4d7251cd51"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Заполним нулевые значения на средние значения по столбцу и приведем к целым числам там, где это необходимо."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "4ed9f5e3b1142d2a"
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "   ModelYear  BodyType  TransmissionType  FuelType  KmMileage  Brand  \\\n0       2016         0                 0         0      69162      0   \n1       2015         0                 0         0      45864      0   \n2       2015         1                 0         0      81506      1   \n3       2013         0                 0         0     115893      0   \n4       2022         2                 0         0      18900      0   \n\n   Clearance  GearNumber  DriveType  SeatsNumber  SellerType  CylinderNumber  \\\n0        998           5          0            5           0               3   \n1        998           5          0            5           0               3   \n2       1198           5          0            5           0               4   \n3        998           5          0            5           0               3   \n4       1462           5          3            7           0               4   \n\n   ValvesNumber  TurboCharger  ListedPrice  \n0             4             0       399600  \n1             4             0       394200  \n2             4             0       454680  \n3             4             0       259200  \n4             4             0      1269000  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ModelYear</th>\n      <th>BodyType</th>\n      <th>TransmissionType</th>\n      <th>FuelType</th>\n      <th>KmMileage</th>\n      <th>Brand</th>\n      <th>Clearance</th>\n      <th>GearNumber</th>\n      <th>DriveType</th>\n      <th>SeatsNumber</th>\n      <th>SellerType</th>\n      <th>CylinderNumber</th>\n      <th>ValvesNumber</th>\n      <th>TurboCharger</th>\n      <th>ListedPrice</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2016</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>69162</td>\n      <td>0</td>\n      <td>998</td>\n      <td>5</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>3</td>\n      <td>4</td>\n      <td>0</td>\n      <td>399600</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2015</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>45864</td>\n      <td>0</td>\n      <td>998</td>\n      <td>5</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>3</td>\n      <td>4</td>\n      <td>0</td>\n      <td>394200</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2015</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>81506</td>\n      <td>1</td>\n      <td>1198</td>\n      <td>5</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>4</td>\n      <td>4</td>\n      <td>0</td>\n      <td>454680</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2013</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>115893</td>\n      <td>0</td>\n      <td>998</td>\n      <td>5</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>3</td>\n      <td>4</td>\n      <td>0</td>\n      <td>259200</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2022</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>18900</td>\n      <td>0</td>\n      <td>1462</td>\n      <td>5</td>\n      <td>3</td>\n      <td>7</td>\n      <td>0</td>\n      <td>4</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1269000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(df.mean(), inplace=True)\n",
    "df = df.astype(int)\n",
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:24.744538600Z",
     "start_time": "2023-12-22T18:07:24.704063Z"
    }
   },
   "id": "29960a84383b6688"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Этап №2. Поиск выбросов\n",
    "\n",
    "Теперь все данные приведены к цифровому виду и с ними можно будет работать. Но сначала необходимо проверить выборку на выбросы. Для этого построим графики и визуально оценим столбцы датафрейма на выбивающиеся значения.\n",
    "\n",
    "Сначала проверим год выпуска автомобиля."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "d697024908a2ede3"
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.ModelYear.plot(kind='hist')\n",
    "plt.title(f'ModelYear')\n",
    "plt.show()\n",
    "\n",
    "df.ModelYear.plot(kind='box')\n",
    "plt.title(f'ModelYear')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:25.454249400Z",
     "start_time": "2023-12-22T18:07:24.731018700Z"
    }
   },
   "id": "109dde16805d47dc"
  },
  {
   "cell_type": "markdown",
   "source": [
    "По графикам видно, что относительно общего количества записей очень мало тех, в которых год выпуска автомобиля раньше 2010. Убедимся в этом, посмотрев количество таких записей и найдя точный год, раньше которого данные можно не принимать в выборку."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "b93796970dbcb5fb"
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "146\n",
      "224\n",
      "343\n",
      "542\n",
      "1241\n",
      "2467\n"
     ]
    }
   ],
   "source": [
    "print(len(df[df.ModelYear < 2005]))\n",
    "print(len(df[df.ModelYear < 2006]))\n",
    "print(len(df[df.ModelYear < 2007]))\n",
    "print(len(df[df.ModelYear < 2008]))\n",
    "print(len(df[df.ModelYear < 2009]))\n",
    "print(len(df[df.ModelYear < 2010]))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:25.460099Z",
     "start_time": "2023-12-22T18:07:25.424332600Z"
    }
   },
   "id": "33962a5e6e3e9f90"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Видим, что можно удалить записи, где год меньше 2009."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "64389c2e1d52d514"
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "df = df[df.ModelYear >= 2009]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:25.465949600Z",
     "start_time": "2023-12-22T18:07:25.450496500Z"
    }
   },
   "id": "28cf4ca1b6d0e516"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Далее перейдем к типу кузова."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a8b95f791994fb7"
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.BodyType.plot(kind='hist')\n",
    "plt.title(f'BodyType')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:25.756845800Z",
     "start_time": "2023-12-22T18:07:25.460099Z"
    }
   },
   "id": "756b762fe66987c4"
  },
  {
   "cell_type": "markdown",
   "source": [
    "По графику видно, что адекватное количество данных присутствует об авто с типом кузова, обозначенным как 0, 1, 2 и 5. Остальные данные можно удалить из выборки, приняв их за выбросы."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "4c7fe8b374494955"
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "df = df[(df.BodyType == 5) | (df.BodyType < 3)]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:25.776457100Z",
     "start_time": "2023-12-22T18:07:25.755832100Z"
    }
   },
   "id": "46c484f7909adcfc"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Далее перейдем к проверке типа трансмиссии."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "1455c1d643edfbf6"
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.TransmissionType.plot(kind='hist')\n",
    "plt.title(f'TransmissionType')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:26.051625Z",
     "start_time": "2023-12-22T18:07:25.765327200Z"
    }
   },
   "id": "d5416b8d5e051ae1"
  },
  {
   "cell_type": "markdown",
   "source": [
    "В данном столбце выбросов не обнаружено, а значит можно перейти к типу топлива."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "814953000bb0055d"
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.FuelType.plot(kind='hist')\n",
    "plt.title(f'FuelType')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:26.516793200Z",
     "start_time": "2023-12-22T18:07:26.051195300Z"
    }
   },
   "id": "9e4e7fcc1538d175"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Мы видим, что популярностью в данной выборке пользуются только дизельные и бензиновые автомобили, а значит, остальные данные можно принять за выбросы."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "7b788cfe1d5429cc"
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [],
   "source": [
    "df = df[df.FuelType > 2]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:26.518967100Z",
     "start_time": "2023-12-22T18:07:26.436320400Z"
    }
   },
   "id": "dfdfd4afa898547c"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Проверим пробег автомобилей."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "2a483c8b7d2cf23d"
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.KmMileage.plot(kind='hist')\n",
    "plt.title(f'KmMileage')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:26.724374100Z",
     "start_time": "2023-12-22T18:07:26.444305600Z"
    }
   },
   "id": "2cfb9deb375c0f1d"
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n",
      "14\n",
      "19\n",
      "36\n",
      "247\n",
      "417\n"
     ]
    }
   ],
   "source": [
    "print(len(df[df.KmMileage > 600000]))\n",
    "print(len(df[df.KmMileage > 500000]))\n",
    "print(len(df[df.KmMileage > 400000]))\n",
    "print(len(df[df.KmMileage > 300000]))\n",
    "print(len(df[df.KmMileage > 200000]))\n",
    "print(len(df[df.KmMileage > 180000]))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:26.729639200Z",
     "start_time": "2023-12-22T18:07:26.713593100Z"
    }
   },
   "id": "b010877274fccca9"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Видим, что можно смело удалять записи, где пробег превосходит 180 000 км."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "5afa1245dd4db37"
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [],
   "source": [
    "df = df[df.KmMileage <= 180000]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:26.841552400Z",
     "start_time": "2023-12-22T18:07:26.729639200Z"
    }
   },
   "id": "1f908c773dfe52f6"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Перейдем к проверке марки автомобиля."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e3cb0e10545ca99e"
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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6c+aMEhISXHPR0dHq37+/Pv74Y0lSZGSkYmJiXPPDhg1TVVWVLly4oH79+nW5fpvtas+8e4/ZnYK9/s/rPJ9r7byCFf0ILPQjsNCPK/PkufFLMBowYMAXjl+8eFGjR4/WnDlz9Mwzz+ijjz5SZmam7Ha75s2bp8bGRjkcDrd9QkND1dTUpMbGRklSWFjYZfOdc5/ft3O7qanJo2B0/fURXX5sT+B0hvu7BJ+h14GFfgQW+hFY6Id3+CUYfZlx48Zp3Lhxru3bb79dDz74oPbu3at58+bJ4XCopaXFbZ+WlhY5nU5XyOlcb/SP8+Hh4bIs67K5zu3wcM9+sdfUXJRlebTLFYWE2BUZGZwBo7a2Ue3tHf4uw6tsts9+yPii1/Ac/Qgs9COw0I8r63yOuiKggtF//dd/qbq6Wvfff79r7NKlSwoNDZUkxcfHq7y83G2fiooKTZgwQf3791dMTIwqKipcb6d98sknqqurU0JCgjo6OlRXV6fq6mpFR0dLko4fP67Y2FhFRHiWsi1LXn/xBfuLOdjr/zK+6DWuHv0ILPQjsNAP7wio+xhZlqXVq1fr4MGDsixLpaWl2rp1q+uv0mbMmKGCggIVFRWptbVV+fn5qqmpUWpqqiQpPT1deXl5qqysVENDg3JycjR69GgNGTJEQ4cOVVJSknJyctTQ0KDKykpt3LhRM2bM8OcpAwCAABJQV4xSU1O1dOlSPfHEEzp79qyio6P1L//yL/re974nSRozZoxWrFjhmo+Li9OmTZsUGRkpScrMzFRbW5tmz56txsZGpaSkaN26da7j5+bmauXKlbr77rtlt9uVlpamjIwMP5wpAAAIRDbL4sKbp6qrfbPGyOkM15TcQh2ruuDdg/vIyBv76bVFd6m2tlFtbdfeGqPo6Aif9Bqeox+BhX4EFvpxZZ3PUVcE1FtpAAAA/kQwAgAAMAhGAAAABsEIAADAIBgBAAAYBCMAAACDYAQAAGAQjAAAAAyCEQAAgEEwAgAAMAhGAAAABsEIAADAIBgBAAAYBCMAAACDYAQAAGAQjAAAAAyCEQAAgEEwAgAAMAhGAAAABsEIAADAIBgBAAAYBCMAAACDYAQAAGB4HIyKi4t9UQcAAIDfeRyMFi1apHvuuUcbNmxQVVWVL2oCAADwC4+D0bvvvqusrCz993//tyZPnqwf/vCH2rNnjy5duuSL+gAAALqNx8God+/emjx5svLy8vTOO+/onnvu0ZYtWzR+/Hg9+eSTKisr80WdAAAAPnfVi69rampUUFCgV199VRUVFUpJSVGfPn00d+5c/fKXv/RmjQAAAN0ixNMdXnvtNe3evVvvvfeebr75ZqWnp+uXv/yloqKiJEkTJ05UZmamfvzjH3u9WAAAAF/yOBg9+eSTmjJlil5++WXdeuutl83fdNNNmjt3rjdqAwAA6FYeB6N3331XlZWViomJkST9+c9/VkREhIYNGyZJio2N1aJFi7xbJQAAQDfweI3Rm2++qbS0NJ04cUKSVFpaqpkzZ+qdd97xdm0AAADdyuMrRuvXr9fGjRtdb6M99NBDiouL01NPPaWJEyd6vUAAAIDu4vEVo//93//VXXfd5TY2fvx4bvYIAACCnsfBaNCgQSosLHQbO3jwoG688UavFQUAAOAPHr+VtmDBAmVmZurb3/62Bg0apKqqKr3xxhtau3atL+oDAADoNh4Ho2nTpumGG27Qq6++qmPHjmngwIHasmWL7rjjDl/UBwAA0G08DkaSlJKSopSUFG/XAgAA4FceB6OzZ88qLy9PJ06cUEdHh9vc1q1bvVYYAABAd/M4GC1dulTV1dX61re+pd69e/uiJgAAAL/wOBgdPXpUr7/+uuuz0QAAAK4VHv+5fkREhK677jpf1AIAAOBXHl8xysjI0NKlSzV//nxFR0e7zXEvIwAAEMw8DkbLly+XJL3xxhuSJJvNJsuyZLPZ9NFHH3m3OgAAgG7kcTB68803fVEHAACA313VR4IMGjRI9fX1OnbsmAYMGKDQ0FANGjTIF/UBAAB0G4+DUU1Nje6//3794Ac/0OOPP67Kykrdc889Ki0t9UV9AAAA3cbjYJSTk6OEhASVlJQoJCREw4YN04IFC/Qf//EfvqgPAACg23gcjIqKirR06VI5HA7ZbDZJ0rx581RRUeH14gAAALqTx8God+/eamlpkSRZliVJamxsVHh4uHcrAwAA6GYeB6NJkyYpKytLJ06ckM1mU01NjZ588klNnDjRF/UBAAB0G4+D0WOPPaawsDDde++9unDhgsaPH6/m5mYtWbLEF/UBAAB0G4/vYxQeHq7c3FydP39ep0+fVmxsrG644QZf1AYAANCtPA5GJSUlbtsnT57UyZMnJUnf+MY3vFMVAACAH3gcjObMmXPZmN1u18CBA7krNgAACGoeB6OysjK37fPnz2vDhg3c+RoAAAQ9jxdff15UVJSysrL061//2hv1AAAA+M1XDkaSVF9fr08//dQbhwIAAPAbj99KW7p0qdt2a2urDh8+rLFjx3qtKAAAAH/wOBh9Xp8+fTRnzhzNmjXLG/UAAAD4jcfBaPXq1b6oAwAAwO88Dkbr16/v0uMeeeQRj4sBAADwJ4+DUXl5ufbt26cRI0bopptu0pkzZ/TBBx/olltucX2QrM1m83qhAAAAvuZxMLLb7Vq6dKkeeOAB19ju3bv19ttva926dd6sDQAAoFt5/Of677zzjmbPnu02NnXqVB08eNBrRQEAAPiDx8EoKirqss9LKywsVGxsrNeKAgAA8AeP30r70Y9+pAULFmjy5Mm68cYbVVlZqbffflu/+MUvfFEfAABAt/E4GM2cOVODBg3S73//e/3lL3/R4MGD9fLLL2v48OG+qA8AAKDbXNUNHseOHauxY8fq/PnzioqK8nZNAAAAfuHxGqPW1lY9++yzSkpK0qRJk1RZWanp06fr3LlzvqgPAACg23gcjNavX6+ioiI999xz6t27t66//nrFxsYqOzvbF/UBAAB0G4+DUUFBgXJzczV+/HjZbDaFhYVp9erVKioq8vibnz9/XqmpqSouLnaNHTlyRDNnzlRiYqImTZqk7du3u+2za9cupaamatSoUUpPT1dpaalrrr29XWvXrtXYsWOVmJiohQsXul3JqqmpUUZGhpKTk5WSkqLs7Gy1tbV5XDcAALg2eRyMmpqaXOuKLMuSJIWGhspu9+xQhw8f1qxZs3Tq1CnXWH19vRYsWKC0tDSVlJQoOztbq1ev1ocffihJKi4u1qpVq7RmzRqVlJTovvvu08KFC9Xc3CxJysvL04EDB7Rz504VFhYqNDRUy5cvdx3/0UcfVVhYmAoLC7Vjxw4dPHhQ+fn5nj4FAADgGuVxMBo1apTr89I6P/rjP//zP3Xbbbd1+Ri7du3SkiVLtHjxYrfxffv2KTIyUrNnz1ZISIjGjBmjadOmadu2bZKk7du3a8qUKUpKSlLv3r01d+5cOZ1O7d271zU/f/58DRw4UH379tWyZcu0f/9+VVZW6uTJkzp06JCysrLkcDg0ePBgZWRkuI4NAADg8V+l/fznP9fcuXO1a9cuNTY26rvf/a4aGxv14osvdvkY48eP17Rp0xQSEuIWjsrLy5WQkOD22Li4OO3YsUOSVFFRoenTp182X1ZWposXL+rMmTNu+0dHR6t///76+OOPJUmRkZGKiYlxzQ8bNkxVVVW6cOGC+vXr1+X6ffFRcMH+8XLBXv/ndZ7PtXZewYp+BBb6EVjox5V58tx4HIyio6P12muv6U9/+pP+/ve/KzY2Vt/85jfVt2/fLh9jwIABXzje2Ngoh8PhNhYaGqqmpqYrzjc2NkqSwsLCLpvvnPv8vp3bTU1NHgWj66+P6PJjewKnM9zfJfgMvQ4s9COw0I/AQj+8w+NgNHXqVP3+97/Xd77zHa8X43A4dPHiRbexlpYWhYeHu+ZbWloum3c6na6Q07ne6PP7W5Z12Vzndufxu6qm5qLM8iqvCQmxKzIyOANGbW2j2ts7/F2GV9lsn/2Q8UWv4Tn6EVjoR2ChH1fW+Rx1xVXd4LG5udmjK0RdlZCQoAMHDriNVVRUKD4+XpIUHx+v8vLyy+YnTJig/v37KyYmRhUVFa630z755BPV1dUpISFBHR0dqqurU3V1taKjoyVJx48fV2xsrCIiPEvZliWvv/iC/cUc7PV/GV/0GlePfgQW+hFY6Id3eLz4OiUlRTNnztS///u/a/369W5fX1Vqaqqqq6uVn5+v1tZWFRUVqaCgwLWuaMaMGSooKFBRUZFaW1uVn5+vmpoapaamSpLS09OVl5enyspKNTQ0KCcnR6NHj9aQIUM0dOhQJSUlKScnRw0NDaqsrNTGjRs1Y8aMr1w3AAC4Nnh8xej06dMaPHiw/va3v+lvf/uba9zmhVVfTqdTW7ZsUXZ2tnJzcxUVFaXly5frzjvvlCSNGTNGK1as0BNPPKGzZ88qLi5OmzZtUmRkpCQpMzNTbW1tmj17thobG5WSkqJ169a5jp+bm6uVK1fq7rvvlt1uV1pamjIyMr5y3QAA4Npgs6yuXXh7+OGH9cILL7i2W1paFBoa6rPCAll1tW/WGDmd4ZqSW6hjVRe8e3AfGXljP7226C7V1jaqre3aW2MUHR3hk17Dc/QjsNCPwEI/rqzzOeqKLr+V9o93mJakCRMmeFYVAABAgPN4jVGnLl5oAgAACBpXHYy8saYIAAAgkFx1MAIAALjWdPmv0tra2vTqq6+6tltbW922JSktLc1LZQEAAHS/Lgej6Oho5ebmuradTqfbts1mIxgBAICg1uVg9NZbb/myDgAAAL9jjREAAIBBMAIAADAIRgAAAAbBCAAAwCAYAQAAGAQjAAAAg2AEAABgEIwAAAAMghEAAIBBMAIAADAIRgAAAAbBCAAAwCAYAQAAGAQjAAAAg2AEAABgEIwAAAAMghEAAIBBMAIAADAIRgAAAAbBCAAAwCAYAQAAGAQjAAAAg2AEAABgEIwAAAAMghEAAIBBMAIAADAIRgAAAAbBCAAAwCAYAQAAGAQjAAAAg2AEAABgEIwAAAAMghEAAIBBMAIAADAIRgAAAAbBCAAAwCAYAQAAGAQjAAAAg2AEAABgEIwAAAAMghEAAIBBMAIAADAIRgAAAAbBCAAAwCAYAQAAGAQjAAAAg2AEAABgEIwAAAAMghEAAIBBMAIAADAIRgAAAAbBCAAAwCAYAQAAGAQjAAAAg2AEAABgEIwAAAAMghEAAIBBMAIAADAIRgAAAAbBCAAAwCAYAQAAGAQjAAAAg2AEAABgEIwAAACMgAxGe/fu1S233KLExETXV1ZWliTpyJEjmjlzphITEzVp0iRt377dbd9du3YpNTVVo0aNUnp6ukpLS11z7e3tWrt2rcaOHavExEQtXLhQ586d69ZzAwAAgSsgg9HRo0f1ve99T6Wlpa6vp556SvX19VqwYIHS0tJUUlKi7OxsrV69Wh9++KEkqbi4WKtWrdKaNWtUUlKi++67TwsXLlRzc7MkKS8vTwcOHNDOnTtVWFio0NBQLV++3J+nCgAAAkjABqNbb731svF9+/YpMjJSs2fPVkhIiMaMGaNp06Zp27ZtkqTt27drypQpSkpKUu/evTV37lw5nU7t3bvXNT9//nwNHDhQffv21bJly7R//35VVlZ26/kBAIDAFOLvAj6vo6NDx44dk8Ph0ObNm9Xe3q6JEydqyZIlKi8vV0JCgtvj4+LitGPHDklSRUWFpk+fftl8WVmZLl68qDNnzrjtHx0drf79++vjjz/W4MGDu1yjzfYVTrAbj9mdgr3+z+s8n2vtvIIV/Qgs9COw0I8r8+S5CbhgdP78ed1yyy2aPHmycnNzVVtbq8cff1xZWVkaMGCAHA6H2+NDQ0PV1NQkSWpsbPzS+cbGRklSWFjYZfOdc111/fURnp7WNc3pDPd3CT5DrwML/Qgs9COw0A/vCLhgFB0d7XprTJIcDoeysrL0gx/8QOnp6WppaXF7fEtLi8LDw12P/aJ5p9PpCkyd642+aP+uqqm5KMvyaJcrCgmxKzIyOANGbW2j2ts7/F2GV9lsn/2Q8UWv4Tn6EVjoR2ChH1fW+Rx1RcAFo7KyMu3Zs0ePPfaYbOba16VLl2S323X77bfr17/+tdvjKyoqFB8fL0mKj49XeXn5ZfMTJkxQ//79FRMTo4qKCtfbaZ988onq6uoue3vuSixLXn/xBfuLOdjr/zK+6DWuHv0ILPQjsNAP7wi4xdeRkZHatm2bNm/erLa2NlVVVempp57S97//fU2ePFnV1dXKz89Xa2urioqKVFBQ4FpXNGPGDBUUFKioqEitra3Kz89XTU2NUlNTJUnp6enKy8tTZWWlGhoalJOTo9GjR2vIkCH+PGUAABAgAu6KUWxsrJ5//nk988wzysvLU58+fTRlyhRlZWWpT58+2rJli7Kzs5Wbm6uoqCgtX75cd955pyRpzJgxWrFihZ544gmdPXtWcXFx2rRpkyIjIyVJmZmZamtr0+zZs9XY2KiUlBStW7fOfycLAAACis2yuPDmqepq36wxcjrDNSW3UMeqLnj34D4y8sZ+em3RXaqtbVRb27W3xig6OsInvYbn6EdgoR+BhX5cWedz1BUB91YaAACAvxCMAAAADIIRAACAQTACAAAwCEYAAAAGwQgAAMAgGAEAABgEIwAAAINgBAAAYBCMAAAADIIRAACAQTACAAAwCEYAAAAGwQgAAMAgGAEAABgEIwAAAINgBAAAYBCMAAAADIIRAACAQTACAAAwCEYAAAAGwQgAAMAgGAEAABgh/i4Awa9Xr+DK1x0dljo6LH+XAQAIQAQjXLUBffuovcNSv34Of5fikbb2DtXXNRGOAACXIRjhqvVzhKiX3aZ/fblUFeca/F1Ol8Td0FfP3Z+o3r17qb2944qPD5SrYVzlAoDuQTDCV1ZxrkHHqi74u4wu8fQql9MZ7uOKuoarXADQPQhG6FGC+SqX3W4jGAGAjxGM0CMF01UuAED3CYwFFAAAAAGAYAQAAGAQjAAAAAyCEQAAgEEwAgAAMAhGAAAABsEIAADAIBgBAAAYBCMAAACDYAQAAGAQjAAAAAyCEQAAgEEwAgAAMEL8XQAA4OrZ7TbZ7Ta/fO9eva7u/607Oix1dFhergbwDoIRAAQpu92m/pFhCrnKgPJVOZ3hV7VfW3uH6uuaCEcISAQjAAhSdrtNIb3s+teXS1VxrsHf5XRJ3A199dz9ibLbbQQjBCSCEQAEuYpzDTpWdcHfZQDXBBZfAwAAGAQjAAAAg2AEAABgEIwAAAAMFl8D8Alf3F/nau+b01XcXwcAwQiA1/nq/jpXe9+cruL+OgAIRgC8jvvrAAhWBCMAPsP9dQAEG4IRAPwDX69j8qZgqhUIFgQjAJA0oG8ftXdY6tfP4e9SAPgRwQgAJPVzhKiX3RZU66K+OXyAsiaP8HcZwDWFYAQA/yCY1kUNG+Dbv9IDeiLeoAYAADAIRgAAAAbBCAAAwGCNEQCg2wXbrQb4uJieg2AEBIlg+kUSTLWiewXrbRH4uJieg2AEBLhg/UUCfJFgvC0CHxfTsxCMgAAXjL9IuL8OriSYbouAnoVgBASJYPpFwv11AAQrFgIAAAAYBCMAAACDYAQAAGAQjAAAAAyCEQAAgNHjglFNTY0yMjKUnJyslJQUZWdnq62tzd9lAQCAANDjgtGjjz6qsLAwFRYWaseOHTp48KDy8/P9XRYAAAgAPeo+RidPntShQ4e0f/9+ORwODR48WBkZGXrqqac0b948f5cHAAhggf5RN5+vj893uzo9KhiVl5crMjJSMTExrrFhw4apqqpKFy5cUL9+/bp0HLtdsrz8WrPZPvvnyBv7yXFdL+8e3EeGDegriZp9jZq7BzV3j2CsOWlIZFB8LI/T6X5j1bb2DjU2tARVOLIsS5a3f8Hq/37Hdumxli8qCFC7d+/Ws88+qz/96U+usVOnTik1NVXvvPOOYmNj/VccAADwu8C+LuhlYWFham5udhvr3A4P5yMMAADo6XpUMIqPj1ddXZ2qq6tdY8ePH1dsbKwiIiL8WBkAAAgEPSoYDR06VElJScrJyVFDQ4MqKyu1ceNGzZgxw9+lAQCAANCj1hhJUnV1tVauXKni4mLZ7XalpaVpyZIl6tUrOBYBAgAA3+lxwQgAAODL9Ki30gAAAP5/CEYAAAAGwQgAAMAgGAEAABgEIwAAAINg5Gc1NTXKyMhQcnKyUlJSlJ2drba2Nn+X1SOdP39eqampKi4udo0dOXJEM2fOVGJioiZNmqTt27f7scKeoaysTA899JBGjx6tcePG6ac//anOnz8viX74w8GDBzVz5kzdcccdGjdunFatWqWWlhZJ9MOf2tvbNWfOHP3sZz9zjdEPL7HgV//8z/9sPfbYY1ZTU5N16tQpa8qUKdamTZv8XVaP8/7771v33HOPlZCQYBUVFVmWZVl1dXXW6NGjrZdeeslqbW213nvvPSsxMdE6cuSIn6u9djU3N1vjxo2znnvuOevTTz+1zp8/b82fP9/60Y9+RD/8oKamxrrtttusnTt3Wu3t7dbZs2etqVOnWs899xz98LN169ZZI0aMsB5//HHLsvh55U1cMfKjkydP6tChQ8rKypLD4dDgwYOVkZGhbdu2+bu0HmXXrl1asmSJFi9e7Da+b98+RUZGavbs2QoJCdGYMWM0bdo0+uNDVVVVGjFihDIzM3XdddfJ6XRq1qxZKikpoR9+EBUVpffee0/p6emy2Wyqq6vTp59+qqioKPrhRwcPHtS+ffv07W9/2zVGP7yHYORH5eXlioyMVExMjGts2LBhqqqq0oULF/xYWc8yfvx4vfHGG/rud7/rNl5eXq6EhAS3sbi4OJWVlXVneT3KzTffrM2bN7vdif7111/XyJEj6Yef9O3bV5I0ceJETZs2TQMGDFB6ejr98JOamhotW7ZMTz/9tBwOh2ucfngPwciPGhsb3V7YklzbTU1N/iipRxowYIBCQkIuG/+i/oSGhtKbbmJZlp599lm9/fbbWrZsGf3ws3379mn//v2y2+1atGgR/fCDjo4OZWVl6aGHHtKIESPc5uiH9xCM/CgsLEzNzc1uY53b4eHh/igJ/8DhcLgWmXZqaWmhN92goaFBixYtUkFBgV566SUNHz6cfvhZaGioYmJilJWVpcLCQvrhB88//7yuu+46zZkz57I5+uE9BCM/io+PV11dnaqrq11jx48fV2xsrCIiIvxYGSQpISFB5eXlbmMVFRWKj4/3U0U9w6lTpzR9+nQ1NDRox44dGj58uCT64Q8ffPCB7r33Xl26dMk1dunSJfXu3VtxcXH0o5vt3r1bhw4dUnJyspKTk7Vnzx7t2bNHycnJ/PfhRQQjPxo6dKiSkpKUk5OjhoYGVVZWauPGjZoxY4a/S4Ok1NRUVVdXKz8/X62trSoqKlJBQYGmT5/u79KuWfX19XrwwQd1xx136IUXXlBUVJRrjn50v+HDh6ulpUVPP/20Ll26pL///e9au3atZsyYocmTJ9OPbvbHP/5RH3zwgd5//329//77mjp1qqZOnar333+f/z68yGZZluXvInqy6upqrVy5UsXFxbLb7UpLS9OSJUvcFp+i+wwfPlxbt25VSkqKJOno0aPKzs7WX//6V0VFRSkjI0Pp6el+rvLa9eKLL2rNmjVyOByy2Wxuc6WlpfTDDyoqKpSTk6OjR48qIiJC06ZNc/3VIP3wr857GK1Zs0YSP6+8hWAEAABg8FYaAACAQTACAAAwCEYAAAAGwQgAAMAgGAEAABgEIwAAAINgBAAAYBCMAAAADIIRAACAQTACAAAwCEYAAADG/wPf7VoJDAY6aAAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Brand.plot(kind='hist')\n",
    "plt.title(f'Brand')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:27.029644100Z",
     "start_time": "2023-12-22T18:07:26.745488600Z"
    }
   },
   "id": "ebf08310d504ca4"
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "527\n"
     ]
    }
   ],
   "source": [
    "print(len(df[df.Brand > 32]))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:27.040889800Z",
     "start_time": "2023-12-22T18:07:27.032830700Z"
    }
   },
   "id": "7387c28612d9c26e"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Можно заметить, что марки обозначенные за цифры 32-44 практически не встречаются в выборке, а значит их можно удалить."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "220ebd1d0e6a4213"
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [],
   "source": [
    "df = df[df.Brand <= 32]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:27.059103800Z",
     "start_time": "2023-12-22T18:07:27.041908500Z"
    }
   },
   "id": "35f27cb227260fb8"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Проверим на выбросы столбец с клиренсем."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c73d6edf2f94bc0e"
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Clearance.plot(kind='box')\n",
    "plt.title(f'Clearance')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:27.222189600Z",
     "start_time": "2023-12-22T18:07:27.049548600Z"
    }
   },
   "id": "18ec7c1f9ac964c4"
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3330\n",
      "1546\n",
      "951\n",
      "817\n",
      "90\n"
     ]
    }
   ],
   "source": [
    "print(len(df[df.Clearance > 2000]))\n",
    "print(len(df[df.Clearance > 2250]))\n",
    "print(len(df[df.Clearance > 2500]))\n",
    "print(len(df[df.Clearance > 2750]))\n",
    "print(len(df[df.Clearance > 3000]))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:27.224536200Z",
     "start_time": "2023-12-22T18:07:27.208725400Z"
    }
   },
   "id": "60963d7e82862c40"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Записи, где значение клиренса превосходит 3000мм можно удалять, приняв их за выбросы."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ec506daa894d8798"
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [],
   "source": [
    "df = df[df.Clearance <= 3000]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:27.247684300Z",
     "start_time": "2023-12-22T18:07:27.217070400Z"
    }
   },
   "id": "34b4bc16a7d9a146"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Проверим столбец с данными о количестве передач."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c85370c0d0e5d692"
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.GearNumber.plot(kind='hist')\n",
    "plt.title(f'GearNumber')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:27.511676400Z",
     "start_time": "2023-12-22T18:07:27.227332700Z"
    }
   },
   "id": "d5077dc3d93bbf04"
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "307\n"
     ]
    }
   ],
   "source": [
    "print(len(df[df.GearNumber > 8]))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:27.530265400Z",
     "start_time": "2023-12-22T18:07:27.514185800Z"
    }
   },
   "id": "b954dc4c190d228e"
  },
  {
   "cell_type": "markdown",
   "source": [
    "В данной выборке совершенно непопулярными оказались автомобили с 9- и 10-ступенчатой коробкой передач, а также автомобили с автоматической трансмиссией."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e3de2d24fd6e81a0"
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [],
   "source": [
    "df = df[(df.GearNumber <= 8) & (df.GearNumber > 1)]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:27.534528Z",
     "start_time": "2023-12-22T18:07:27.520650300Z"
    }
   },
   "id": "cc6dd3927ef9b374"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Проверим также данные о типе привода."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "d56d6f2f7c7f421d"
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.DriveType.plot(kind='hist')\n",
    "plt.title(f'DriveType')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:27.801879400Z",
     "start_time": "2023-12-22T18:07:27.534528Z"
    }
   },
   "id": "90a01da1f7c41ead"
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1731\n",
      "1380\n",
      "1288\n"
     ]
    }
   ],
   "source": [
    "print(len(df[df.DriveType == 1]))\n",
    "print(len(df[df.DriveType == 2]))\n",
    "print(len(df[df.DriveType == 3]))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:27.826419600Z",
     "start_time": "2023-12-22T18:07:27.800865400Z"
    }
   },
   "id": "ad803799f71c5868"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Несмотря на то, что передний привод в разы популярнее других видов, никакие записи в данном случае нельзя принять за выбросы. Поэтому перейдем к проверке количества сидений."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "1e9540dcbaeaff5e"
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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XLIIR0MGZTC7y9vGU2z+FIV9f+77fzxnq6ht0sbKacASgUyEYAR2cyeQiN1eTHvxTgYrPVTm7nFYJur6nnp4dKpPJhWAEoFMhGAGdRPG5Kh0vveTsMgDgmtbxFyoAAAA4CMEIAADAQDACAAAwEIwAAAAMBCMAAAADwQgAAMBAMAIAADAQjAAAAAwEIwAAAAPBCAAAwEAwAgAAMBCMAAAADAQjAAAAA8EIAADAQDACAAAwEIwAAAAMBCMAAAADwQgAAMBAMAIAADAQjAAAAAwEIwAAAAPBCAAAwEAwAgAAMBCMAAAADAQjAAAAA8EIAADAQDACAAAwEIwAAAAMBCMAAAADwQgAAMBAMAIAADAQjAAAAAwEIwAAAAPBCAAAwEAwAgAAMBCMAAAADAQjAAAAA8EIAADAQDACAAAwEIwAAAAMBCMAAACDU4JRYWGh7rnnHoWHh2v8+PFavHixKioqJEkrVqzQiBEjFBoaav15+eWXrcfu2LFDUVFRGjVqlGJiYlRQUGAdq6+v17p16xQZGanQ0FDFx8fr3Llz1vHy8nIlJCRozJgxioiIUGpqqurq6hx34gAAoENzeDCqqalRXFycQkND9f777+uNN95QZWWlHnnkEUnSsWPHlJKSooKCAuvPrFmzJEl5eXlKSUnR2rVrlZ+frzvuuEPx8fG6cuWKJCkjI0MHDx7UK6+8otzcXLm7u2v58uXW5160aJE8PT2Vm5ur7OxsHTp0SJmZmY5uAQAA6KAcHoxKS0t1ww03KDExUd27d5evr69mzZql/Px8Xb16VZ9//rlGjBjR4rHbt2/X1KlTFRYWpm7dumnevHny9fXVrl27rOPz589Xv3791LNnTy1btkwHDhxQSUmJTp06pSNHjig5OVkeHh4aOHCgEhISlJWV5cjTBwAAHZibo59wyJAh2rp1a7N977zzjoYPH67CwkLV1dUpPT1dR48elZeXl2bMmKG4uDiZTCYVFxdrxowZzY4NCgpSYWGhLl++rDNnzigkJMQ65ufnJ29vb504cUKS5OPjI39/f+t4YGCgSktLdenSJfXq1avV5+Di8p+ceevmbI+5Oyt6cm1wxO+Pa8UWPbFFT1rWFfpiz7k5PBj9M4vFog0bNmjfvn166aWXVFZWpvDwcM2dO1dPPvmk/va3vykxMVEmk0lxcXEym83y8PBoNoe7u7uqq6tlNpslSZ6enjbjTWP/emzTdnV1tV3BqE8fL7vPtSPM3VnRk87L17eHQ5+Pa8UWPbFFT1pGXxo5LRhVVVVp6dKlOn78uF566SUNHTpUQ4cO1fjx462PGTlypO6++27t2rVLcXFx8vDwUE1NTbN5ampq5Ovraw05TeuN/nm8R48eslgsNmNN2z162PfHu7z8siwWuw75t1xcGi/K9pi7s6InjVxdTQ4PGG3lwgWz6usb2v15uFZs0RNb9KRlXaEvTefYGk4JRqdPn9b8+fPVv39/ZWdnq3fv3pKkvXv3qqysTLNnz7Y+9urVq3J3d5ckBQcHq6ioqNlcxcXFuvnmm+Xt7S1/f38VFxdbX047f/68KisrFRISooaGBlVWVqqsrEx+fn6SpJMnTyogIEBeXvalZItF7XbxtOfcnRU96dwc+bvjWrFFT2zRk5bRl0YOX3x98eJF3X333Ro9erSef/55ayiSGl9aW7NmjQ4dOiSLxaKCggK98MIL1nelxcbGKicnR4cPH1Ztba0yMzNVXl6uqKgoSVJMTIwyMjJUUlKiqqoqpaWlKTw8XIMGDdLgwYMVFhamtLQ0VVVVqaSkRJs2bVJsbKyjWwAAADooh98xevXVV1VaWqq33npLb7/9drOxgoICLV26VCtXrtTZs2fl5+en+++/X3feeackady4cVqxYoV1PCgoSFu2bJGPj48kKTExUXV1dZozZ47MZrMiIiK0YcMG6/zp6elavXq1br31VplMJkVHRyshIcFRpw4AADo4F4uFG2f2KitrnzVGfn5e7TJ3Z0VPGrm5Na4xmpqeq+Oll5xdTqsM799Lbz5wky5cMKuuzjFrjLhWmqMntuhJy7pCX5rOsTX4ShAAAAADwQgAAMBAMAIAADAQjAAAAAwEIwAAAAPBCAAAwEAwAgAAMBCMAAAADAQjAAAAA8EIAADAQDACAAAwEIwAAAAMBCMAAAADwQgAAMBAMAIAADAQjAAAAAwEIwAAAAPBCAAAwEAwAgAAMBCMAAAADAQjAAAAA8EIAADAQDACAAAwEIwAAAAMBCMAAAADwQgAAMBAMAIAADAQjAAAAAwEIwAAAAPBCAAAwEAwAgAAMBCMAAAADAQjAAAAA8EIAADAQDACAAAwEIwAAAAMBCMAAAADwQgAAMBAMAIAADAQjAAAAAwEIwAAAAPBCAAAwEAwAgAAMBCMAAAADAQjAAAAA8EIAADAQDACAAAwEIwAAAAMBCMAAAADwQgAAMBAMAIAADA4JRgVFhbqnnvuUXh4uMaPH6/FixeroqJCkvTxxx9r5syZCg0N1aRJk7R9+/Zmx+7YsUNRUVEaNWqUYmJiVFBQYB2rr6/XunXrFBkZqdDQUMXHx+vcuXPW8fLyciUkJGjMmDGKiIhQamqq6urqHHPSAACgw3N4MKqpqVFcXJxCQ0P1/vvv64033lBlZaUeeeQRXbx4UQsWLFB0dLTy8/OVmpqqNWvW6JNPPpEk5eXlKSUlRWvXrlV+fr7uuOMOxcfH68qVK5KkjIwMHTx4UK+88opyc3Pl7u6u5cuXW5970aJF8vT0VG5urrKzs3Xo0CFlZmY6ugUAAKCDcngwKi0t1Q033KDExER1795dvr6+mjVrlvLz87V79275+Phozpw5cnNz07hx4zR9+nRlZWVJkrZv366pU6cqLCxM3bp107x58+Tr66tdu3ZZx+fPn69+/fqpZ8+eWrZsmQ4cOKCSkhKdOnVKR44cUXJysjw8PDRw4EAlJCRY5wYAAHBz9BMOGTJEW7dubbbvnXfe0fDhw1VUVKSQkJBmY0FBQcrOzpYkFRcXa8aMGTbjhYWFunz5ss6cOdPseD8/P3l7e+vEiROSJB8fH/n7+1vHAwMDVVpaqkuXLqlXr16tPgcXl1Y/1O4522PuzoqeXBsc8fvjWrFFT2zRk5Z1hb7Yc24OD0b/zGKxaMOGDdq3b59eeuklvfDCC/Lw8Gj2GHd3d1VXV0uSzGbzd46bzWZJkqenp81409i/Htu0XV1dbVcw6tPHq9WPtVd7zt1Z0ZPOy9e3h0Ofj2vFFj2xRU9aRl8aOS0YVVVVaenSpTp+/LheeuklDR06VB4eHrp8+XKzx9XU1KhHj8Y/rh4eHqqpqbEZ9/X1tYacpvVG/3q8xWKxGWvabpq/tcrLL8tiseuQf8vFpfGibI+5Oyt60sjV1eTwgNFWLlwwq76+od2fh2vFFj2xRU9a1hX60nSOreGUYHT69GnNnz9f/fv3V3Z2tnr37i1JCgkJ0cGDB5s9tri4WMHBwZKk4OBgFRUV2YzffPPN8vb2lr+/v4qLi60vp50/f16VlZUKCQlRQ0ODKisrVVZWJj8/P0nSyZMnFRAQIC8v+1KyxaJ2u3jac+7Oip50bo783XGt2KIntuhJy+hLI4cvvr548aLuvvtujR49Ws8//7w1FElSVFSUysrKlJmZqdraWh0+fFg5OTnWdUWxsbHKycnR4cOHVVtbq8zMTJWXlysqKkqSFBMTo4yMDJWUlKiqqkppaWkKDw/XoEGDNHjwYIWFhSktLU1VVVUqKSnRpk2bFBsb6+gWAACADsrhd4xeffVVlZaW6q233tLbb7/dbKygoEDbtm1Tamqq0tPT1bt3by1fvlxjx46VJI0bN04rVqzQypUrdfbsWQUFBWnLli3y8fGRJCUmJqqurk5z5syR2WxWRESENmzYYJ0/PT1dq1ev1q233iqTyaTo6GglJCQ46tQBAEAH52KxcOPMXmVl7bPGyM/Pq13m7qzoSSM3t8Y1RlPTc3W89JKzy2mV4f176c0HbtKFC2bV1TlmjRHXSnP0xBY9aVlX6EvTObYGXwkCAABgIBgBAAAYCEYAAAAGghEAAICBYAQAAGAgGAEAABgIRgAAAAaCEQAAgIFgBAAAYCAYAQAAGAhGAAAABoIRAACAgWAEAABgIBgBAAAYCEYAAAAGghEAAICBYAQAAGAgGAEAABgIRgAAAAaCEQAAgIFgBAAAYCAYAQAAGAhGAAAABruDUV5eXnvUAQAA4HR2B6MHHnhAkydP1u9//3uVlpa2R00AAABOYXcwev/995WcnKxPP/1UU6ZM0W9+8xu98cYbunr1anvUBwAA4DB2B6Nu3bppypQpysjI0P79+zV58mRt27ZNEyZM0KpVq1RYWNgedQIAALS7/3jxdXl5uXJycvTaa6+puLhYERERuu666zRv3jxt3ry5LWsEAABwCDd7D3jzzTf1+uuv669//auGDBmimJgYbd68Wb1795YkTZw4UYmJifrtb3/b5sUCAAC0J7uD0apVqzR16lT96U9/0ogRI2zGf/zjH2vevHltURsAAIBD2R2M3n//fZWUlMjf31+S9NFHH8nLy0uBgYGSpICAAD3wwANtWyUAAIAD2L3G6N1331V0dLS+/PJLSVJBQYFmzpyp/fv3t3VtAAAADmX3HaONGzdq06ZN1pfR7rnnHgUFBenxxx/XxIkT27xAAAAAR7H7jtE333yjm266qdm+CRMm8GGPAACg07M7GA0YMEC5ubnN9h06dEj9+/dvs6IAAACcwe6X0hYsWKDExETddtttGjBggEpLS7Vnzx6tW7euPeoDAABwGLuD0fTp03X99dfrtdde0/Hjx9WvXz9t27ZNo0ePbo/6AAAAHMbuYCRJERERioiIaOtaAAAAnMruYHT27FllZGToyy+/VENDQ7OxF154oc0KAwAAcDS7g9HSpUtVVlamn/3sZ+rWrVt71AQAAOAUdgejY8eO6Z133rF+NxoAAMC1wu6363t5eal79+7tUQsAAIBT2X3HKCEhQUuXLtX8+fPl5+fXbIzPMgIAAJ2Z3cFo+fLlkqQ9e/ZIklxcXGSxWOTi4qK//e1vbVsdAACAA9kdjN599932qAMAAMDp/qOvBBkwYIAuXryo48ePq2/fvnJ3d9eAAQPaoz4AAACHsTsYlZeXa/bs2brrrru0ZMkSlZSUaPLkySooKGiP+gAAABzG7mCUlpamkJAQ5efny83NTYGBgVqwYIH+53/+pz3qAwAAcBi7g9Hhw4e1dOlSeXh4yMXFRZIUFxen4uLiNi8OAADAkewORt26dVNNTY0kyWKxSJLMZrN69OjRtpUBAAA4mN3BaNKkSUpOTtaXX34pFxcXlZeXa9WqVZo4cWJ71AcAAOAwdgej3/3ud/L09NTPf/5zXbp0SRMmTNCVK1eUlJRk95NXVFQoKipKeXl51n0rVqzQiBEjFBoaav15+eWXreM7duxQVFSURo0apZiYmGaLvuvr67Vu3TpFRkYqNDRU8fHxOnfunHW8vLxcCQkJGjNmjCIiIpSamqq6ujq76wYAANcmuz/HqEePHkpPT1dFRYW++uorBQQE6Prrr7f7iY8ePaqHH35Yp0+fbrb/2LFjSklJ0S9/+UubY/Ly8pSSkqItW7Zo5MiRysrKUnx8vPbt2ycPDw9lZGTo4MGDeuWVV+Tl5aVHH31Uy5cv13PPPSdJWrRokfz9/ZWbm6uysjLFx8crMzNTcXFxdtcPAACuPXbfMcrPz1d+fr5Onjypb7/9VqdOnbLua60dO3YoKSlJDz30ULP9V69e1eeff64RI0a0eNz27ds1depUhYWFqVu3bpo3b558fX21a9cu6/j8+fPVr18/9ezZU8uWLdOBAwdUUlKiU6dO6ciRI0pOTpaHh4cGDhyohIQEZWVl2dsCAABwjbL7jtHcuXNt9plMJvXr16/Vn4o9YcIETZ8+XW5ubs3CUWFhoerq6pSenq6jR4/Ky8tLM2bMUFxcnEwmk4qLizVjxoxmcwUFBamwsFCXL1/WmTNnFBISYh3z8/OTt7e3Tpw4IUny8fGRv7+/dTwwMFClpaW6dOmSevXq1eoeGG/Ga1NNc7bH3J0VPbk2OOL3x7Vii57Yoict6wp9sefc7A5GhYWFzbYrKir0+9//3q5Pvu7bt2+L+y9fvqzw8HDNnTtXTz75pP72t78pMTFRJpNJcXFxMpvN8vDwaHaMu7u7qqurZTabJUmenp42401j/3ps03Z1dbVdwahPH69WP9Ze7Tl3Z0VPOi9fX8e+W5VrxRY9sUVPWkZfGtkdjP5V7969lZycrClTpug3v/nND5pr/PjxGj9+vHV75MiRuvvuu7Vr1y7FxcXJw8PD+lEBTWpqauTr62sNOVeuXLEZ79GjhywWi81Y07a9HzVQXn5ZxicVtBkXl8aLsj3m7qzoSSNXV5PDA0ZbuXDBrPr6hnZ/Hq4VW/TEFj1pWVfoS9M5tsYPDkaSdPHiRX377bc/eJ69e/eqrKxMs2fPtu67evWq3N3dJUnBwcEqKipqdkxxcbFuvvlmeXt7y9/fX8XFxdaX086fP6/KykqFhISooaFBlZWVKisrk5+fnyTp5MmTCggIkJeXfSnZYlG7XTztOXdnRU86N0f+7rhWbNETW/SkZfSlkd3BaOnSpc22a2trdfToUUVGRv7gYiwWi9asWaMf/ehHGjt2rD766CO98MIL1ueMjY1VYmKibr/9doWFhSkrK0vl5eWKioqSJMXExCgjI0M33nijfH19lZaWpvDwcA0aNEiSFBYWprS0NK1evVoXLlzQpk2bFBsb+4PrBgAA14YffMfouuuu09y5czVr1qwfXExUVJSWLl2qlStX6uzZs/Lz89P999+vO++8U5I0btw4rVixwjoeFBSkLVu2yMfHR5KUmJiouro6zZkzR2azWREREdqwYYN1/vT0dK1evVq33nqrTCaToqOjlZCQ8IPrBgAA1wYXi4UbZ/YqK2ufNUZ+fl7tMndnRU8aubk1rjGamp6r46WXnF1Oqwzv30tvPnCTLlwwq67OMWuMuFaaoye26EnLukJfms6xNey+Y7Rx48ZWPW7hwoX2Tg0AAOBUdgejoqIi7d69WzfccIN+/OMf68yZM/rwww81bNgw67u7XK7lD0MAAADXLLuDkclk0tKlS/XrX//auu/111/Xvn37mq3nAQAA6Gzs/kqQ/fv3a86cOc32TZs2TYcOHWqzogAAAJzB7mDUu3dvm+9Fy83NVUBAQJsVBQAA4Ax2v5R23333acGCBZoyZYr69++vkpIS7du3T88880x71AcAAOAwdgejmTNnasCAAdq5c6c+++wzDRw4UH/60580dOjQ9qgPAADAYf6jD3iMjIxUZGSkKioq1Lt377auCQAAwCnsXmNUW1urp556SmFhYZo0aZJKSko0Y8YMnTt3rj3qAwAAcBi7g9HGjRt1+PBhPf300+rWrZv69OmjgIAApaamtkd9AAAADmP3S2k5OTn64x//KH9/f7m4uMjT01Nr1qyxfpErAABAZ2X3HaPq6mrruqKmr1lzd3eXyWT3VAAAAB2K3Wlm1KhR1u9La/rqjxdffFE33nhj21YGAADgYHa/lPbII49o3rx52rFjh8xms37xi1/IbDbrD3/4Q3vUBwAA4DB2ByM/Pz+9+eabeu+99/T1118rICBAt9xyi3r27Nke9QEAADiM3cFo2rRp2rlzp26//fb2qAcAAMBp/qMV01euXGnrOgAAAJzO7jtGERERmjlzpm6++WZdf/31zcYWLlzYZoUBAAA4mt3B6KuvvtLAgQP197//XX//+9+t+5veoQYAANBZtToY3XvvvXr++ef14osvSpJqamrk7u7eboUBAAA4WqvXGBUUFDTbvvnmm9u8GAAAAGf6jz+uuulTrwEAAK4V/3EwYk0RAAC41vAFZwAAAIZWL76uq6vTa6+9Zt2ura1tti1J0dHRbVQWAACA47U6GPn5+Sk9Pd267evr22zbxcWFYAQAADq1Vgejv/zlL+1ZBwAAgNOxxggAAMBAMAIAADAQjAAAAAwEIwAAAAPBCAAAwEAwAgAAMBCMAAAADAQjAAAAA8EIAADAQDACAAAwEIwAAAAMBCMAAAADwQgAAMBAMAIAADAQjAAAAAwEIwAAAAPBCAAAwEAwAgAAMBCMAAAADAQjAAAAA8EIAADAQDACAAAwEIwAAAAMBCMAAACDU4NRRUWFoqKilJeXZ9338ccfa+bMmQoNDdWkSZO0ffv2Zsfs2LFDUVFRGjVqlGJiYlRQUGAdq6+v17p16xQZGanQ0FDFx8fr3Llz1vHy8nIlJCRozJgxioiIUGpqqurq6tr/RAEAQKfgtGB09OhRzZo1S6dPn7buu3jxohYsWKDo6Gjl5+crNTVVa9as0SeffCJJysvLU0pKitauXav8/Hzdcccdio+P15UrVyRJGRkZOnjwoF555RXl5ubK3d1dy5cvt86/aNEieXp6Kjc3V9nZ2Tp06JAyMzMdet4AAKDjckow2rFjh5KSkvTQQw8127979275+Phozpw5cnNz07hx4zR9+nRlZWVJkrZv366pU6cqLCxM3bp107x58+Tr66tdu3ZZx+fPn69+/fqpZ8+eWrZsmQ4cOKCSkhKdOnVKR44cUXJysjw8PDRw4EAlJCRY5wYAAHBzxpNOmDBB06dPl5ubW7NwVFRUpJCQkGaPDQoKUnZ2tiSpuLhYM2bMsBkvLCzU5cuXdebMmWbH+/n5ydvbWydOnJAk+fj4yN/f3zoeGBio0tJSXbp0Sb169Wp1/S4urT9Xe+dsj7k7K3pybXDE749rxRY9sUVPWtYV+mLPuTklGPXt27fF/WazWR4eHs32ubu7q7q6+t+Om81mSZKnp6fNeNPYvx7btF1dXW1XMOrTx6vVj7VXe87dWdGTzsvXt4dDn49rxRY9sUVPWkZfGjklGH0XDw8PXb58udm+mpoa9ejRwzpeU1NjM+7r62sNOU3rjf71eIvFYjPWtN00f2uVl1+WxWLXIf+Wi0vjRdkec3dW9KSRq6vJ4QGjrVy4YFZ9fUO7Pw/Xii16YouetKwr9KXpHFujQwWjkJAQHTx4sNm+4uJiBQcHS5KCg4NVVFRkM37zzTfL29tb/v7+Ki4utr6cdv78eVVWViokJEQNDQ2qrKxUWVmZ/Pz8JEknT55UQECAvLzsS8kWi9rt4mnPuTsretK5OfJ3x7Vii57Yoictoy+NOtTnGEVFRamsrEyZmZmqra3V4cOHlZOTY11XFBsbq5ycHB0+fFi1tbXKzMxUeXm5oqKiJEkxMTHKyMhQSUmJqqqqlJaWpvDwcA0aNEiDBw9WWFiY0tLSVFVVpZKSEm3atEmxsbHOPGUAANCBdKg7Rr6+vtq2bZtSU1OVnp6u3r17a/ny5Ro7dqwkady4cVqxYoVWrlyps2fPKigoSFu2bJGPj48kKTExUXV1dZozZ47MZrMiIiK0YcMG6/zp6elavXq1br31VplMJkVHRyshIcEJZwoAADoiF4uFG2f2KitrnzVGfn5e7TJ3Z0VPGrm5Na4xmpqeq+Oll5xdTqsM799Lbz5wky5cMKuuzjFrjLhWmqMntuhJy7pCX5rOsTU61EtpAAAAzkQwAgAAMBCMAAAADAQjAAAAA8EIAADAQDACAAAwEIwAAAAMBCMAAAADwQgAAMBAMAIAADAQjAAAAAwEIwAAAAPBCAAAwEAwAgAAMBCMAAAADAQjAAAAA8EIAADAQDACAAAwEIwAAAAMBCMAAAADwQgAAMBAMAIAADAQjAAAAAwEIwAAAAPBCAAAwEAwAgAAMBCMAAAADAQjAAAAA8EIAADAQDACAAAwEIwAAAAMBCMAAAADwQgAAMBAMAIAADAQjAAAAAwEIwAAAAPBCAAAwEAwAgAAMBCMAAAADAQjAAAAA8EIAADAQDACAAAwEIwAAAAMBCMAAAADwQgAAMBAMAIAADAQjAAAAAwEIwAAAAPBCAAAwEAwAgAAMBCMAAAADB0yGO3atUvDhg1TaGio9Sc5OVmS9PHHH2vmzJkKDQ3VpEmTtH379mbH7tixQ1FRURo1apRiYmJUUFBgHauvr9e6desUGRmp0NBQxcfH69y5cw49NwAA0HF1yGB07Ngx3XnnnSooKLD+PP7447p48aIWLFig6Oho5efnKzU1VWvWrNEnn3wiScrLy1NKSorWrl2r/Px83XHHHYqPj9eVK1ckSRkZGTp48KBeeeUV5ebmyt3dXcuXL3fmqQIAgA7EzdkFtOTYsWO6/fbbbfbv3r1bPj4+mjNnjiRp3Lhxmj59urKysjRy5Eht375dU6dOVVhYmCRp3rx5evnll7Vr1y7NmDFD27dvV1JSkvr16ydJWrZsmSZMmKCSkhINHDiw1fW5uLTBSX7HnO0xd2dFT64Njvj9ca3Yoie26EnLukJf7Dm3DheMGhoadPz4cXl4eGjr1q2qr6/XxIkTlZSUpKKiIoWEhDR7fFBQkLKzsyVJxcXFmjFjhs14YWGhLl++rDNnzjQ73s/PT97e3jpx4oRdwahPH68fcIbOm7uzoiedl69vD4c+H9eKLXpii560jL406nDBqKKiQsOGDdOUKVOUnp6uCxcuaMmSJUpOTlbfvn3l4eHR7PHu7u6qrq6WJJnN5u8cN5vNkiRPT0+b8aax1iovvyyLxd4z+34uLo0XZXvM3VnRk0auriaHB4y2cuGCWfX1De3+PFwrtuiJLXrSsq7Ql6ZzbI0OF4z8/PyUlZVl3fbw8FBycrLuuusuxcTEqKamptnja2pq1KNHD+tjWxr39fW1Bqam9UYtHd9aFova7eJpz7k7K3rSuTnyd8e1Youe2KInLaMvjTrc4uvCwkKtX79eln/67Vy9elUmk0kjR45UUVFRs8cXFxcrODhYkhQcHPyd497e3vL391dxcbF17Pz586qsrLR5eQ4AAHRNHS4Y+fj4KCsrS1u3blVdXZ1KS0v1+OOP65e//KWmTJmisrIyZWZmqra2VocPH1ZOTo51XVFsbKxycnJ0+PBh1dbWKjMzU+Xl5YqKipIkxcTEKCMjQyUlJaqqqlJaWprCw8M1aNAgZ54yAADoIDrcS2kBAQF69tln9eSTTyojI0PXXXedpk6dquTkZF133XXatm2bUlNTlZ6ert69e2v58uUaO3aspMZ3qa1YsUIrV67U2bNnFRQUpC1btsjHx0eSlJiYqLq6Os2ZM0dms1kRERHasGGD804WAAB0KC4WC68o2qusrH0WX/v5ebXL3J0VPWnk5ta4+Hpqeq6Ol15ydjmtMrx/L735wE26cMGsujrHLL7mWmmOntiiJy3rCn1pOsfW6HAvpQEAADgLwQgAAMBAMAIAADAQjAAAAAwEIwAAAAPBCAAAwEAwAgAAMBCMAAAADAQjAAAAA8EIAADAQDACAAAwEIwAAAAMBCMAAAADwQgAAMDg5uwCAKCjMJlcZDK5OLsMuzQ0WNTQYHF2GcA1g2AEAGoMRd4+nnJz7Vw30uvqG3SxsppwBLQRghEAqDEYubma9OCfClR8rsrZ5bRK0PU99fTsUJlMLgQjoI0QjADgnxSfq9Lx0kvOLgOAk3Sue8YAAADtiGAEAABgIBgBAAAYCEYAAAAGghEAAICBYAQAAGAgGAEAABgIRgAAAAaCEQAAgIFgBAAAYCAYAQAAGAhGAAAABoIRAACAgWAEAABgIBgBAAAYCEYAAAAGghEAAICBYAQAAGAgGAEAABgIRgAAAAaCEQAAgIFgBAAAYCAYAQAAGAhGAAAABoIRAACAgWAEAABgIBgBAAAYCEYAAAAGghEAAICBYAQAAGAgGAEAABjcnF0AgGuXq6tj/+31Q57P0bW2pe+rvSOeV0ODRQ0NFmeXAbSoywWj8vJyPfroozpy5IhcXV11xx13aMmSJXJz63KtANpN357Xqb7Bol69PBz6vL6+PRz6fM7Wmj53xJ7U1TfoYmU14QgdUpdLA4sWLZK/v79yc3NVVlam+Ph4ZWZmKi4uztmlAdeMXh5ucjW56ME/Faj4XJWzy2mVW4b2VfKUG5xdhl06Y5+Dru+pp2eHymRyIRihQ+pSwejUqVM6cuSIDhw4IA8PDw0cOFAJCQl6/PHHCUZAOyg+V6XjpZecXUarBPbteHdWWqsz9Rno6LpUMCoqKpKPj4/8/f2t+wIDA1VaWqpLly6pV69erZrHZJIsbfwPHReXxv91czO1+dztyWL5R+1trb160p41t4emNSLD+/eSR3dXJ1fTOoF9e0qi5vbWGWse4tcYQJ2x9umH/E3pbH83pNbX3JH++2OxWGRphyLs+d25WNqjgg7q9ddf11NPPaX33nvPuu/06dOKiorS/v37FRAQ4LziAACA03W8tyu0I09PT125cqXZvqbtHj067210AADQNrpUMAoODlZlZaXKysqs+06ePKmAgAB5eXk5sTIAANARdKlgNHjwYIWFhSktLU1VVVUqKSnRpk2bFBsb6+zSAABAB9Cl1hhJUllZmVavXq28vDyZTCZFR0crKSlJrq6dY+EiAABoP10uGAEAAHyXLvVSGgAAwPchGAEAABgIRgAAAAaCEQAAgIFgBAAAYCAYOVl5ebkSEhI0ZswYRUREKDU1VXV1dc4uy+kKCwt1zz33KDw8XOPHj9fixYtVUVHh7LI6hPr6es2dO1cPP/yws0txusrKSi1evFgRERH66U9/qoSEBJ07d87ZZTnd8ePHNWfOHI0ZM0YTJkzQY489pqtXrzq7LKeoqKhQVFSU8vLyrPs+/vhjzZw5U6GhoZo0aZK2b9/uxAqdo6W+vPPOO7rzzjs1evRoTZo0SRs3blRDQ4MTq3QOgpGTLVq0SJ6ensrNzVV2drYOHTqkzMxMZ5flVDU1NYqLi1NoaKjef/99vfHGG6qsrNQjjzzi7NI6hI0bN+qDDz5wdhkdwv3336/q6mrt2bNH+/btk6urqx599FFnl+VUDQ0Nuu+++zRlyhQdOXJE2dnZev/997VlyxZnl+ZwR48e1axZs3T69GnrvosXL2rBggWKjo5Wfn6+UlNTtWbNGn3yySdOrNSxWurLp59+qsWLF2vRokX64IMPtGXLFr366qtd8r9HBCMnOnXqlI4cOaLk5GR5eHho4MCBSkhIUFZWlrNLc6rS0lLdcMMNSkxMVPfu3eXr66tZs2YpPz/f2aU53aFDh7R7927ddtttzi7F6T799FN9/PHHWrt2rXr16qWePXsqJSVFSUlJzi7NqS5evKjz58+roaHB+i3lJpNJHh4eTq7MsXbs2KGkpCQ99NBDzfbv3r1bPj4+mjNnjtzc3DRu3DhNnz69y/zd/a6+fP3115o9e7Z+9rOfyWQyKTAwUFFRUV3y7y7ByImKiork4+Mjf39/677AwECVlpbq0qVLTqzMuYYMGaKtW7c2+zTyd955R8OHD3diVc5XXl6uZcuW6Yknnuhy/5FrySeffKKgoCD9+c9/VlRUlCZMmKB169apb9++zi7NqXx9fTVv3jytW7dON954oyZOnKjBgwdr3rx5zi7NoSZMmKA9e/boF7/4RbP9RUVFCgkJabYvKChIhYWFjizPab6rL1OmTNHSpUut2zU1NXrvvfe65N9dgpETmc1mm//ANW1XV1c7o6QOx2Kx6KmnntK+ffu0bNkyZ5fjNA0NDUpOTtY999yjG264wdnldAgXL17UiRMn9OWXX2rHjh167bXXdPbsWS1ZssTZpTlVQ0OD3N3d9eijj+qjjz7SG2+8oZMnTyo9Pd3ZpTlU37595ebmZrO/pb+77u7uXeZv7nf15Z9VVVUpMTFR7u7uXS5QSwQjp/L09NSVK1ea7Wva7tGjhzNK6lCqqqr0wAMPKCcnRy+99JKGDh3q7JKc5tlnn1X37t01d+5cZ5fSYXTv3l2StGzZMvXs2VN+fn5atGiR9u/fL7PZ7OTqnGfPnj1655139Ktf/Urdu3dXcHCwEhMT9cc//tHZpXUIHh4eqqmpabavpqaGv7mGL774QrNnz1ZdXZ1eeOEF9ezZ09klOdz3x0a0q+DgYFVWVqqsrEx+fn6SpJMnTyogIEBeXl5Ors65Tp8+rfnz56t///7Kzs5W7969nV2SU73++us6d+6cxowZI0nWP+x79+7tsguxg4KC1NDQoNraWl133XWSZH0HTVf+CshvvvnG5h1obm5u6tatm5Mq6lhCQkJ08ODBZvuKi4sVHBzspIo6jv379+u///u/ddddd+l3v/vdv72zdK3ijpETDR48WGFhYUpLS1NVVZVKSkq0adMmxcbGOrs0p7p48aLuvvtujR49Ws8//3yXD0WS9Pbbb+vDDz/UBx98oA8++EDTpk3TtGnTumwokqTIyEgNHDhQjzzyiMxmsyoqKvTUU09p8uTJXfJfuU0mTJig8+fPa/Pmzaqvr1dJSYkyMjI0ffp0Z5fWIURFRamsrEyZmZmqra3V4cOHlZOToxkzZji7NKf66KOPlJiYqKVLl2rJkiVdNhRJBCOnS09PV11dnW699Vbddddduummm5SQkODsspzq1VdfVWlpqd566y2FhYUpNDTU+gM06datm1588UW5urpqypQpmjJligICApSWlubs0pwqKChIzz77rP7yl78oIiJCv/71rzVp0iSbdyF1Vb6+vtq2bZvefvttRUREaPny5Vq+fLnGjh3r7NKcavPmzaqrq1Nqamqzv7lxcXHOLs3hXCxd+Z4zAADAP+GOEQAAgIFgBAAAYCAYAQAAGAhGAAAABoIRAACAgWAEAABgIBgBAAAYCEYAAAAGghEAAICBYAQAAGAgGAEAABj+P5gUq7EgdA/gAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.SeatsNumber.plot(kind='hist')\n",
    "plt.title(f'SeatsNumber')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:28.138393300Z",
     "start_time": "2023-12-22T18:07:27.810548500Z"
    }
   },
   "id": "a85238adbd0e8c1c"
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "289\n",
      "28640\n",
      "383\n",
      "3246\n",
      "327\n"
     ]
    }
   ],
   "source": [
    "print(len(df[df.SeatsNumber < 5]))\n",
    "print(len(df[df.SeatsNumber == 5]))\n",
    "print(len(df[df.SeatsNumber == 6]))\n",
    "print(len(df[df.SeatsNumber == 7]))\n",
    "print(len(df[df.SeatsNumber > 7]))\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:28.153529500Z",
     "start_time": "2023-12-22T18:07:28.138393300Z"
    }
   },
   "id": "43b3f60241bcb1f7"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Записи, где количество сидений не равно 5 или 7 можно принять за выбросы."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "4f5f18d3e2f7cf6a"
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [],
   "source": [
    "df = df[(df.SeatsNumber == 5) | (df.SeatsNumber == 7)]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:28.280031600Z",
     "start_time": "2023-12-22T18:07:28.153529500Z"
    }
   },
   "id": "a805fe0fc7d71fb"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Проверим на выбросы столбец с типов продавца."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "9bbfd94f7c7278fe"
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.SellerType.plot(kind='hist')\n",
    "plt.title(f'SellerType')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:28.469015700Z",
     "start_time": "2023-12-22T18:07:28.179561700Z"
    }
   },
   "id": "65e6762e000a0d7f"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Выбросов не обнаружено, поэтому переходим к количеству цилиндров."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "75a884a585e55862"
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.CylinderNumber.plot(kind='hist')\n",
    "plt.title(f'CylinderNumber')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:28.985285200Z",
     "start_time": "2023-12-22T18:07:28.470816300Z"
    }
   },
   "id": "da5663775d943ece"
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "14\n",
      "6691\n",
      "24744\n",
      "60\n",
      "352\n",
      "25\n"
     ]
    }
   ],
   "source": [
    "print(len(df[df.CylinderNumber == 0]))\n",
    "print(len(df[df.CylinderNumber == 1]))\n",
    "print(len(df[df.CylinderNumber == 2]))\n",
    "print(len(df[df.CylinderNumber == 3]))\n",
    "print(len(df[df.CylinderNumber == 4]))\n",
    "print(len(df[df.CylinderNumber == 5]))\n",
    "print(len(df[df.CylinderNumber == 6]))\n",
    "print(len(df[df.CylinderNumber == 7]))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:29.006958700Z",
     "start_time": "2023-12-22T18:07:28.992947400Z"
    }
   },
   "id": "818271b887ebfb0c"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Здесь можно удалить записи, в которых количество цилиндров не равно 3, 4 или 6"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "8e708b25b2c9d691"
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [],
   "source": [
    "df = df[(df.CylinderNumber == 3) | (df.CylinderNumber == 4) | (df.CylinderNumber == 6)]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:29.122338400Z",
     "start_time": "2023-12-22T18:07:29.006958700Z"
    }
   },
   "id": "4360523b8eb4b8d0"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Далее проверим столбец с количеством клапаном на одном цилиндре."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "efea18380b619434"
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1020\n",
      "856\n",
      "29906\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "df.ValvesNumber.plot(kind='box')\n",
    "plt.title(f'ValvesNumber')\n",
    "plt.show()\n",
    "print(len(df[df.ValvesNumber == 2]))\n",
    "print(len(df[df.ValvesNumber == 3]))\n",
    "print(len(df[df.ValvesNumber == 4]))\n",
    "print(len(df[df.ValvesNumber == 5]))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:29.229658400Z",
     "start_time": "2023-12-22T18:07:29.017301200Z"
    }
   },
   "id": "fc269e3d5bab7658"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Удалим записи, где количество клапанов не равно 4."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "abf02c91de91bd2e"
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [],
   "source": [
    "df = df[df.ValvesNumber == 4]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:29.233853900Z",
     "start_time": "2023-12-22T18:07:29.188962100Z"
    }
   },
   "id": "d21354f5edaecf13"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Проверим столбец с наличием турбонаддува."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "8a2b6ea1cade99b8"
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.TurboCharger.plot(kind='hist')\n",
    "plt.title(f'TurboCharger')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:29.492614300Z",
     "start_time": "2023-12-22T18:07:29.196805900Z"
    }
   },
   "id": "9db4a62a1bbec6f9"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Здесь выбросов также не обнаружено. \n",
    "\n",
    "Последним проверим на выброс столбец с ценами на автомобили."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e0159c33d8a5a108"
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.ListedPrice.plot(kind='box')\n",
    "plt.title(f'ListedPrice')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:29.665662300Z",
     "start_time": "2023-12-22T18:07:29.492079900Z"
    }
   },
   "id": "b6db6702f34cb9af"
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df[df.ListedPrice < 1e8]\n",
    "df.ListedPrice.plot(kind='box')\n",
    "plt.title(f'ListedPrice')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:29.843069500Z",
     "start_time": "2023-12-22T18:07:29.665662300Z"
    }
   },
   "id": "56f445d6d294d4ab"
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1747\n",
      "723\n",
      "359\n"
     ]
    }
   ],
   "source": [
    "print(len(df[df.ListedPrice > 0.2e7]))\n",
    "print(len(df[df.ListedPrice > 0.3e7]))\n",
    "print(len(df[df.ListedPrice > 0.4e7]))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:29.869480400Z",
     "start_time": "2023-12-22T18:07:29.846436100Z"
    }
   },
   "id": "ba5749f6b46a5983"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Здесь за выбросы можно принять записи, где стоимость автомобиля превышает 3 миллиона рублей."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "494a7c1320bab496"
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [
    {
     "data": {
      "text/plain": "29183"
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df.ListedPrice <= 0.3e7]\n",
    "len(df)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:30.007734600Z",
     "start_time": "2023-12-22T18:07:29.855657800Z"
    }
   },
   "id": "2c4d6916e74c96fc"
  },
  {
   "cell_type": "markdown",
   "source": [
    "По итогу чистки выборки от выбросов количество записей сократилось с 37814 до 29183 значений.\n",
    "\n",
    "Но используемые далее модели не смогут предсказать точную стоимость автомобиля до рубля, поэтому необходимо объединить значения колонки ListedPrice в интервалы и обозначить их так же, как и другие качественные характеристики. Подобно реальному рыночному разделению, автомобили будут разделены на 3 категории: бюджетные (до 1млн рублей), среднего бюджета (1млн-2млн рублей) и дорогие (более 2млн рублей)."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "5fb44b7a55055497"
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [],
   "source": [
    "df.loc[df['ListedPrice'] <= 1_000_000, 'ListedPrice'] = 0\n",
    "df.loc[(1_000_000 < df['ListedPrice']) & (df['ListedPrice'] <= 2_000_000), 'ListedPrice'] = 1\n",
    "df.loc[df['ListedPrice'] > 2_000_000, 'ListedPrice'] = 2\n",
    "\n",
    "df = df.rename(columns={'ListedPrice': 'PriceCategory'})"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:30.007734600Z",
     "start_time": "2023-12-22T18:07:29.876890900Z"
    }
   },
   "id": "17b43c98a499cc9a"
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "d0b985ae34f72543"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Этап №3. Применение моделей\n",
    "\n",
    "Для начала необходимо разделить данные на те, по которым будем выдвигаться решение, и те, с которыми это решение будет сравниваться.  "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "7ae9ff973a9ed1d"
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [],
   "source": [
    "x = df[['ModelYear',\n",
    "        'BodyType', \n",
    "        'TransmissionType',\n",
    "        'FuelType',\n",
    "        'KmMileage',\n",
    "        'Brand', \n",
    "        'Clearance', \n",
    "        'GearNumber', \n",
    "        'DriveType', \n",
    "        'SeatsNumber',\n",
    "        'SellerType',\n",
    "        'CylinderNumber',\n",
    "        'ValvesNumber',\n",
    "        'TurboCharger']]\n",
    "y = df[['PriceCategory']]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:30.007734600Z",
     "start_time": "2023-12-22T18:07:29.888030Z"
    }
   },
   "id": "977d94734eb225d1"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Теперь разделим данные на наборы для обучения и теста. Для обучения будем использовать 75% данных, для тестов - 25%. Также сразу данные необходимо стандартизировать под размеры датафрейма."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "dfa4fd053d43a502"
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.25, shuffle=True)\n",
    "\n",
    "standardise = StandardScaler()\n",
    "x_train_scale = standardise.fit_transform(x_train)\n",
    "x_train_scale = pd.DataFrame(x_train_scale)\n",
    "x_test_scale = standardise.transform(x_test)\n",
    "x_test_scale = pd.DataFrame(x_test_scale)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:30.007734600Z",
     "start_time": "2023-12-22T18:07:29.900449700Z"
    }
   },
   "id": "245ad68736aa5d18"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Метод k ближайших соседей\n",
    "\n",
    "Первой моделью используем метод k ближайших соседей. \n",
    "KNN или K-ближайших соседей — это алгоритм классификации, который основывается на близости объектов. Он определяет класс нового объекта, анализируя ближайших к нему соседей и выбирая наиболее часто встречающийся класс среди этих соседей.\n",
    "\n",
    "Подберем оптимальные гиперпараметры, а именно, количество соседей k с помощью простого перебора, используя встроенную функцию range, подставляя предлагаемые значения в модель."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "b8c1208fdd5e891d"
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy = []\n",
    "num_neigh = []\n",
    "\n",
    "for i in range(1, 16):\n",
    "    KNN = KNeighborsClassifier(n_neighbors=i)\n",
    "    KNN.fit(x_train_scale, np.ravel(y_train,order='C'))\n",
    "    accuracy.append(KNN.score(x_test_scale, y_test))\n",
    "    num_neigh += [i]\n",
    "\n",
    "plt.scatter(num_neigh, accuracy)\n",
    "plt.xlabel('Number of heighbours')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.show();  "
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:46.685305400Z",
     "start_time": "2023-12-22T18:07:29.924639500Z"
    }
   },
   "id": "7df5a89cd24a67ac"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Для того чтобы модель k ближайших соседей работала оптимально и была готова к переобучению, необходимо взять как можно меньшее количество соседей. Поэтому мы остановимся на значении 4."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "fede865d93f36906"
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [
    {
     "data": {
      "text/plain": "   RealPriceCategory  PredictPriceCategory  is_True\n0                  0                     0     True\n1                  0                     0     True\n2                  1                     1     True\n3                  0                     0     True\n4                  0                     0     True",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>RealPriceCategory</th>\n      <th>PredictPriceCategory</th>\n      <th>is_True</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>1</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN = KNeighborsClassifier(n_neighbors=4)\n",
    "KNN = KNN.fit(x_test_scale,np.ravel(y_test,order='C'))\n",
    "\n",
    "\n",
    "start_time = time.time()\n",
    "predict_PriceCategory_KNN = KNN.predict(x_test_scale)\n",
    "result_KNN = {\"RealPriceCategory\": list(y_test.PriceCategory), \"PredictPriceCategory\": list(predict_PriceCategory_KNN)}\n",
    "result_KNN = pd.DataFrame(result_KNN)\n",
    "result_KNN[\"is_True\"] = result_KNN[\"RealPriceCategory\"] == result_KNN[\"PredictPriceCategory\"]\n",
    "\n",
    "end_time = time.time()\n",
    "\n",
    "KNN_time = end_time - start_time\n",
    "KNN_train_accuracy = max(accuracy) \n",
    "KNN_test_accuracy = sum(result_KNN[\"is_True\"]) / len(result_KNN[\"is_True\"]) \n",
    "\n",
    "result_KNN.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:47.510895100Z",
     "start_time": "2023-12-22T18:07:46.678057400Z"
    }
   },
   "id": "f7a0d85ce4cf0c9a"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Метод решающих деревьев\n",
    "\n",
    "Структура дерева представляет собой «листья» и «ветки». На рёбрах («ветках») дерева решения записаны признаки, от которых зависит целевая функция, в «листьях» записаны значения целевой функции, а в остальных узлах — признаки, по которым различаются случаи. Чтобы классифицировать новый случай, надо спуститься по дереву до листа и выдать соответствующее значение. \n",
    "\n",
    "Далее подберем гиперпараметры для метода решающих деревьев аналогично предыдущему методу - с помощью перебора."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "1931c46163a20a4b"
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy = []\n",
    "depth = []\n",
    "\n",
    "for i in range(1,15):\n",
    "    DT = DecisionTreeClassifier(max_depth=i)\n",
    "    DT.fit(x_train_scale, y_train)\n",
    "    accuracy.append(DT.score(x_test_scale, y_test))\n",
    "    depth.append(i)\n",
    "\n",
    "plt.scatter(depth,accuracy)\n",
    "plt.xlabel('Max depth')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.show();"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:49.323701300Z",
     "start_time": "2023-12-22T18:07:47.511473600Z"
    }
   },
   "id": "b59ecbf55595b70b"
  },
  {
   "cell_type": "markdown",
   "source": [
    "В данном случае наоборот - чем больше число глубины, тем легче потом будет переобучать модель. Поэтому остановимся на числе 10."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "eb79153301892966"
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [
    {
     "data": {
      "text/plain": "   RealPriceCategory  PredictPriceCategory  is_True\n0                  0                     0     True\n1                  0                     0     True\n2                  1                     1     True\n3                  0                     0     True\n4                  0                     0     True",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>RealPriceCategory</th>\n      <th>PredictPriceCategory</th>\n      <th>is_True</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>1</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DT = DecisionTreeClassifier(max_depth=10)\n",
    "DT = DT.fit(x_test_scale,y_test)\n",
    "\n",
    "\n",
    "start_time = time.time()\n",
    "predict_PriceCategory_DT = DT.predict(x_test_scale)\n",
    "result_DT = {\"RealPriceCategory\": list(y_test.PriceCategory), \"PredictPriceCategory\": list(predict_PriceCategory_DT)}\n",
    "result_DT = pd.DataFrame(result_DT)\n",
    "result_DT[\"is_True\"] = result_DT[\"RealPriceCategory\"] == result_DT[\"PredictPriceCategory\"]\n",
    "end_time = time.time()\n",
    "\n",
    "DT_time = end_time - start_time\n",
    "DT_train_accuracy = max(accuracy) \n",
    "DT_test_accuracy = sum(result_DT[\"is_True\"]) / len(result_DT[\"is_True\"]) \n",
    "\n",
    "result_DT.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:49.436777400Z",
     "start_time": "2023-12-22T18:07:49.329676200Z"
    }
   },
   "id": "dd3bc9b1ec7e7467"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Метод случайных лесов\n",
    "\n",
    "Лес состоит из ансамбля решающих деревьев. Лес создает деревья решений для случайно выбранных семплов данных, получает прогноз от каждого дерева и выбирает наилучшее решение посредством голосования.\n",
    "\n",
    "Для него подберём гиперпараметры (n_estimators и max_features) с помощью GridSearchCV."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "7bf784dd4d9a9a23"
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'n_estimators': [25, 50, 100, 150],\n",
    "    'max_features': ['sqrt', 'log2', None]\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(RandomForestClassifier(), param_grid)\n",
    "grid_search.fit(x_train, np.ravel(y_train,order='C'))\n",
    "print(grid_search.best_params_) "
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:49.436777400Z",
     "start_time": "2023-12-22T18:07:49.430727700Z"
    }
   },
   "id": "7eed6f51ffe4e23b"
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [
    {
     "data": {
      "text/plain": "   RealPriceCategory  PredictPriceCategory  is_True\n0                  0                     0     True\n1                  0                     0     True\n2                  1                     1     True\n3                  0                     0     True\n4                  0                     0     True",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>RealPriceCategory</th>\n      <th>PredictPriceCategory</th>\n      <th>is_True</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>1</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF = RandomForestClassifier(n_estimators=50, max_features=None, max_depth=12)\n",
    "RF.fit(x_train, np.ravel(y_train,order='C'))\n",
    "\n",
    "RF_train = RF.predict(x_train)\n",
    "\n",
    "start_time = time.time()\n",
    "predict_PriceCategory_RF = RF.predict(x_test)\n",
    "result_RF = {\"RealPriceCategory\": list(y_test.PriceCategory), \"PredictPriceCategory\": list(predict_PriceCategory_RF)}\n",
    "result_RF = pd.DataFrame(result_RF)\n",
    "result_RF[\"is_True\"] = result_RF[\"RealPriceCategory\"] == result_RF[\"PredictPriceCategory\"]\n",
    "end_time = time.time()\n",
    "\n",
    "RF_time = end_time - start_time\n",
    "RF_train_accuracy = accuracy_score(y_train, RF_train) \n",
    "RF_test_accuracy = accuracy_score(y_test, predict_PriceCategory_RF) \n",
    "\n",
    "result_RF.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:53.745127500Z",
     "start_time": "2023-12-22T18:07:49.436777400Z"
    }
   },
   "id": "9bb440c51a85d8e5"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Этап №4. Визуализация и конкурентный анализ моделей\n",
    "\n",
    "Для начала построим матрицы ошибок для всех моделей."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "832aff5431ffae37"
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица ошибок для KNN\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Axes: xlabel='PredictPriceCategory', ylabel='RealPriceCategory'>"
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq = pd.crosstab(result_KNN[\"RealPriceCategory\"], result_KNN[\"PredictPriceCategory\"])\n",
    "print(\"Матрица ошибок для KNN\")\n",
    "sns.heatmap(freq,annot=True,fmt=\"d\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:54.166376900Z",
     "start_time": "2023-12-22T18:07:53.745127500Z"
    }
   },
   "id": "7bacd21ecbace1a7"
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица ошибок для решающих деревьев\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Axes: xlabel='PredictPriceCategory', ylabel='RealPriceCategory'>"
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq = pd.crosstab(result_DT[\"RealPriceCategory\"], result_DT[\"PredictPriceCategory\"])\n",
    "print(\"Матрица ошибок для решающих деревьев\")\n",
    "sns.heatmap(freq,annot=True,fmt=\"d\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:54.484215300Z",
     "start_time": "2023-12-22T18:07:54.128917500Z"
    }
   },
   "id": "48ebf566152721e5"
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица ошибок для случайных лесов\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Axes: xlabel='PredictPriceCategory', ylabel='RealPriceCategory'>"
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq = pd.crosstab(result_RF[\"RealPriceCategory\"], result_RF[\"PredictPriceCategory\"])\n",
    "print(\"Матрица ошибок для случайных лесов\")\n",
    "sns.heatmap(freq,annot=True,fmt=\"d\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:54.815656700Z",
     "start_time": "2023-12-22T18:07:54.484215300Z"
    }
   },
   "id": "19d34fe719e95b5"
  },
  {
   "cell_type": "markdown",
   "source": [
    "После работы всех моделей были сохранены метаданные - их точность при обучении и тестах, а также время работы. Приведём все данные в удобный вид и добавим еще несколько оценочных параметров: точность (precision), полноту (recall) и метрику f1. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "cdaed9ea4db3fa9e"
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "  model      time  train_accuracy  test_accuracy  precision    recall  \\\n0   KNN  0.782792        0.871711       0.892544   0.847787  0.685359   \n1    DT  0.047931        0.877193       0.912966   0.889105  0.764867   \n2    RF  0.108331        0.922968       0.879660   0.793066  0.675651   \n\n         f1  \n0  0.746078  \n1  0.814887  \n2  0.721951  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>model</th>\n      <th>time</th>\n      <th>train_accuracy</th>\n      <th>test_accuracy</th>\n      <th>precision</th>\n      <th>recall</th>\n      <th>f1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>KNN</td>\n      <td>0.782792</td>\n      <td>0.871711</td>\n      <td>0.892544</td>\n      <td>0.847787</td>\n      <td>0.685359</td>\n      <td>0.746078</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>DT</td>\n      <td>0.047931</td>\n      <td>0.877193</td>\n      <td>0.912966</td>\n      <td>0.889105</td>\n      <td>0.764867</td>\n      <td>0.814887</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>RF</td>\n      <td>0.108331</td>\n      <td>0.922968</td>\n      <td>0.879660</td>\n      <td>0.793066</td>\n      <td>0.675651</td>\n      <td>0.721951</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_list = [KNN_time, DT_time, RF_time]\n",
    "train_accuracy_list = [KNN_train_accuracy, DT_train_accuracy, RF_train_accuracy]\n",
    "test_accuracy_list = [KNN_test_accuracy, DT_test_accuracy, RF_test_accuracy]\n",
    "\n",
    "precision_list = [precision_score(y_test, predict_PriceCategory_KNN, average=\"macro\"),\n",
    "                  precision_score(y_test, predict_PriceCategory_DT, average=\"macro\"),\n",
    "                  precision_score(y_test, predict_PriceCategory_RF, average=\"macro\")\n",
    "                  ]\n",
    "recall_list = [recall_score(y_test, predict_PriceCategory_KNN, average=\"macro\"),\n",
    "               recall_score(y_test, predict_PriceCategory_DT, average=\"macro\"),\n",
    "               recall_score(y_test, predict_PriceCategory_RF, average=\"macro\"),\n",
    "               ]\n",
    "\n",
    "f1_list = [f1_score(y_test, predict_PriceCategory_KNN, average=\"macro\"),\n",
    "           f1_score(y_test, predict_PriceCategory_DT, average=\"macro\"),\n",
    "           f1_score(y_test, predict_PriceCategory_RF, average=\"macro\")\n",
    "           ]\n",
    "\n",
    "metrics = {\n",
    "    \"model\": [\"KNN\", \"DT\", \"RF\"],\n",
    "    \"time\": time_list,\n",
    "    \"train_accuracy\": train_accuracy_list,\n",
    "    \"test_accuracy\": test_accuracy_list,\n",
    "    \"precision\": precision_list,\n",
    "    \"recall\": recall_list,\n",
    "    \"f1\": f1_list\n",
    "}\n",
    "metrics = pd.DataFrame(metrics)\n",
    "\n",
    "metrics.plot()\n",
    "plt.show()\n",
    "metrics"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-22T18:07:55.266253500Z",
     "start_time": "2023-12-22T18:07:54.815656700Z"
    }
   },
   "id": "e4140beeee78333e"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Далее определим наилучшую модель следующим образом: за лучший результат по каждому показателю даётся 3 балла, за второй - 2 балла, за худший - 1 балл.\n",
    "\n",
    "KNN: 1 + 1 + 2 + 2 + 1 + 2 = 9 \n",
    "DT: 2 + 2 + 3 + 3 + 3 + 3 = 16\n",
    "RF: 3 + 3 + 1 + 1 + 2 + 1 = 11"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "15c30666f7214550"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Выводы \n",
    "\n",
    "Метод k - ближайших соседей является одним из простейших методов машинного обучения, однако несмотря на это, он достаточно неплохо справился с поставленной задачей. Случайные леса состоят из ансамбля решающих деревьев, но несмотря на это, имеют худшие показатели эффективности, чем сам метод решающих деревьев. Данный метод превзошел остальные модели практически по всем показателям, что самое главное - по доле верных ответов на тестовом наборе данных. Так что можно с уверенностью утверждать, что решающие деревья наилучшим образом подходят для решения данной задачи."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "1e340e8d1c021566"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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